Actual value and historical data chart for India Agriculture Value Added Annual Percent Growth

India IN: GDP: Growth: Gross Value Added: Agriculture, Forestry, and Fishing data was reported at 1.440 % in 2024. This records a decrease from the previous number of 4.707 % for 2023. India IN: GDP: Growth: Gross Value Added: Agriculture, Forestry, and Fishing data is updated yearly, averaging 2.669 % from Mar 1962 (Median) to 2024, with 63 observations. The data reached an all-time high of 15.640 % in 1989 and a record low of -12.775 % in 1980. India IN: GDP: Growth: Gross Value Added: Agriculture, Forestry, and Fishing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Gross Domestic Product: Annual Growth Rate. Annual growth rate for agricultural, forestry, and fishing value added based on constant local currency. Aggregates are based on constant 2015 prices, expressed in U.S. dollars. Agriculture corresponds to ISIC divisions 01-03 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 4.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;Note: Data for OECD countries are based on ISIC, revision 4.

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.

India Real Gross Capital Formation Growth: Agriculture data was reported at 1.583 % in 2018. This records a decrease from the previous number of 13.250 % for 2017. India Real Gross Capital Formation Growth: Agriculture data is updated yearly, averaging 4.376 % from Mar 1952 (Median) to 2018, with 67 observations. The data reached an all-time high of 51.963 % in 1991 and a record low of -31.223 % in 1992. India Real Gross Capital Formation Growth: Agriculture data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIU002: Memo Items: Investment of Agriculture Sector. Data prior to 2013 is 2004-2005 base

The Agriculture Industry in India Report is Segmented by Commodity Type (Cereals and Grains, Pulses and Oilseed, and More). The Report Includes Production Analysis (Volume), Consumption Analysis (Value and Volume), Export Analysis (Value and Volume), Import Analysis (Value and Volume), and Price Trend Analysis. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Metric Tons).

India Agricultural Production: Major Crops: Achievements: Pulses data was reported at 27.504 Ton mn in 2023. This records an increase from the previous number of 27.302 Ton mn for 2022. India Agricultural Production: Major Crops: Achievements: Pulses data is updated yearly, averaging 12.840 Ton mn from Mar 1956 (Median) to 2023, with 68 observations. The data reached an all-time high of 27.504 Ton mn in 2023 and a record low of 8.350 Ton mn in 1967. India Agricultural Production: Major Crops: Achievements: Pulses data remains active status in CEIC and is reported by Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIB002: Agricultural Production: Targets & Achievement of Major Crops.

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.

The dataset contains year- and crop-wise historically compiled all india data on the area of agricultural lands used for growing different types of food, non-food and other types of agricultural crops such as rice, jowar, bajra, maize, ragi/marua, wheat, barley, other cereals and millets, tur or arhar, other pulses, sugarcane, condiments, spices, fruits, vegetables, groundnut, castor, sesamum, mustard, lin, rapeseed, cotton, jute, indigo, opium, tobacco, tea, coffee, and other crops.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from India were selected based on the following criterion: (a) Corn growers in Davanagere, Belgaum, Warangal, Kurnool (all = districts) Location: Davanegere, Belgaum, Warangal, Kurnool
Average adapter of innovation
Mechanized tillage operation due labour shortage
Keeps animals for milk
Corn forage is used for animal feeding
Keep update on commercial market trend Secondary source of Agriculture income is dairy Relies on high fertilizer use. (Farmers who use >2 bags of urea and 1 bag of DAP per acre is considered as High fertilizer use growers) Low use of crop protection products (aim for growers who don't use CPP, if not possible, low use --> UPDATED: maximum of 2 sprays!) Traditional way of weed control (bullock drawn harrow followed by ridging)

(b) Cotton growers in WC & South
Location: Yavatmal , Akola, Aurangabad, Jalgaon, Warangal , Kurnool , Karimnagar (= all districts)
Commercial, normally traditional practices but a few always looks for new products. (Use hybrids and are interested in new products which deliver higher yields, with less disease and pests.)
Very particular about quality seed.
High expectation of profit from farming.
Good investment on inputs for getting maximum returns.
Some irrigation available but not sufficient, Manual operations.
Social and seeks knowledge from other fellow farmers and retailers. Western regions: I take all decisions in terms of cotton production by myself, without consulting fellow farmers, retailers, agronomists or sales representatives (based on answers of RF) Use generic / branded chemistry Dependent on retailers to fund his crop protection chemicals
Prefer Cotton hybrid which give good re flushing
Rotation with Bengalgram

(c) Rice growers in North & East Location: Karnal, Ludhiana, Sri Muktsar Sahib, Patiala, Allahabad,Gorakhpur, Barabanki (North & East)
Commercial ,Average adapter of innovation.
Medium input cost. (Spend 300 - 500 Rs on fertilizers, About 400-500 Rs on CP products can be considered as moderate or medium input cost.)
Mechanized tillage operations due to shortage of labour.
Good use of CP products. (Use products of leading MNCs; new chemistry/new products etc) Very particular about quality seeds.
Always look forward to new technologies that would reduce costs or increase profits.
High expectation of profits from farming.
Good investment on input for getting maximum returns.
Not aware about soil fertility issues.
Use generic chemicals
Dependent on commission agent for his recurring expenses or retailer to fund his inputs. = ALL BACKGROUND INFO
May or may not own a tractor.
High involvement of retailer/ commission agent on his decision of CP inputs Rice wheat rotation.

(d) Rice growers in East Location: Ranchi, Raipur (= west), Burdwan, Midnapore , Bhagalpur . (= East)
Late adapter of innovation . --> UPDATED: Western region (Raipur): BF is not late adapter of innovation (based on answer of RF) Usage of hybrid Rice or traditional varieties . (Either Open Pollinated Varieties or certified hybrids is fine. )
Moderate usage of CP products . (The spend on CP products is relatively lower i.e. less number of sprays or lower dose of recommended CP products. ) = ALL BACKGROUND INFO
Lack of resources ( irrigation, finance ) ,less educated ,traditional (= background info),low financial status .
Primarily dependent on farm for food and income. --> RF in Raipur (western region) says to not depend on his farm for income but BF will be recruited based on the original screening criteria above
not aware about soil fertility . --> UPDATED: in western region: BF are aware about soil fertility (based on answer of RF) --> UPDATED: Eastern region (Jharkhand & Bihar): BF are aware about soil fertility (based on answer of RF)
Depends on fertilizer for enhancing productivity.
Usage of generic chemistry. May or may not own tractor. High involvement of retailer on his decision of CP inputs . --> RF in Raipur (western region) says to take all decisions himself but BF will be recruited based on the original screening criteria above
Migrated farmers adopt technology . = ALL BACKGROUND INFO
Traditional cultivation practice. (This generally means OPV, little fertilizers and little chemicals.) = ALL BACKGROUND INFO
Conversion happening from OP to hybrid seeds in rainfed areas. = ALL BACKGROUND INFO

(e) Tomato growers location: Nasik, Pune, Ahmednagar, Belgaum, Vadodara, Jaipur.
Early adapter of innovation.
Mechanized tillage operations due to labour shortage.
Very particular about quality Seeds.
Always look forward to new CP technologies to increase profit
Good crop knowledge & Use advance chemistry ( Farmers who use newly launched, high performance CP products from leading MNCs can be considered as "Advance" or new chemistry products.). --> UPDATED: in Western regions: only have a little bit of knowledge about this and use only a little bit (based on answers of RF)
Use of SYT tomato seeds & CP products. (only for RF, BF can use SYT products but not necessarily) = ALL BACKGROUND INFO, is asked in screening but nobody is screened out (!)
Keep updates on commercial market trend .
Irrigated farms
Has milch animals. --> UPDATED: in Western regions, not all should have livestock (based on answer of RF)
Brand loyalty
Commercially very active.
Knows market prices in leading cities.
Has relationship with market forces.
Keeps in touch with other progressive farmers, good retailers and company professionals.

(f) Soybean growers location: Ratlam, Dhar, Hoshangabad, Washim Follow traditional cultivation practices . (Usually the use of farm-saved seeds and varieties, do not use adequate fertilizers, follow traditional interculture practices etc.)
Limited technical knowledge.
Many use farm saved seed.
Mechanized tillage and spraying operation.
Use of tractor for sowing and threshing operations.
Low investment on input in comparison with actual requirement.
Farmers are members of co-operative society in some areas. = ALL BACKGROUND INFO
Soyabean wheat rotation
Some involvement of retailer/commission agent on his decision of CP inputs.

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a.

The statistic shows the growth of the real gross domestic product (GDP) in India from 2020 to 2024, with projections up until 2030. GDP refers to the total market value of all goods and services that are produced within a country per year. It is an important indicator of the economic strength of a country. Real GDP is adjusted for price changes and is therefore regarded as a key indicator for economic growth. In 2024, India's real gross domestic product growth was at about 6.46 percent compared to the previous year. Gross domestic product (GDP) growth rate in India Recent years have witnessed a shift of economic power and attention to the strengthening economies of the BRIC countries: Brazil, Russia, India, and China. The growth rate of gross domestic product in the BRIC countries is overwhelmingly larger than in traditionally strong economies, such as the United States and Germany. While the United States can claim the title of the largest economy in the world by almost any measure, China nabs the second-largest share of global GDP, with India racing Japan for third-largest position. Despite the world-wide recession in 2008 and 2009, India still managed to record impressive GDP growth rates, especially when most of the world recorded negative growth in at least one of those years. Part of the reason for India’s success is the economic liberalization that started in 1991and encouraged trade subsequently ending some public monopolies. GDP growth has slowed in recent years, due in part to skyrocketing inflation. India’s workforce is expanding in the industry and services sectors, growing partially because of international outsourcing — a profitable venture for the Indian economy. The agriculture sector in India is still a global power, producing more wheat or tea than anyone in the world except for China. However, with the mechanization of a lot of processes and the rapidly growing population, India’s unemployment rate remains relatively high.

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Agriculture Analytics Market Size 2024-2028

The agriculture analytics market size is forecast to increase by USD 1.23 billion at a CAGR of 12.97% between 2023 and 2028. The market is experiencing significant growth due to the increasing demand for food production to meet the needs of a growing global population. Infrastructure development, including the integration of artificial intelligence (AI) and smart farming technologies, is a key driver in this market. Smart farming practices, such as field planning and irrigation management, are becoming increasingly important for sustainable agricultural production. However, the high cost of implementing analytics in agriculture remains a challenge for many farmers and agribusinesses, particularly in developing countries. Despite this, the long-term benefits of using data analytics insights to optimize farming practices are expected to outweigh the initial investment costs. By utilizing AI and other advanced technologies, farmers can optimize their operations, improve crop yields, and reduce water usage, ultimately leading to more sustainable and profitable farming practices. The market is expected to continue growing as more farmers adopt these technologies to meet the demands of a changing agricultural landscape.

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The market is witnessing significant growth as farmers and agricultural businesses seek to enhance their productivity and efficiency. This market is driven by the increasing demand for food production to meet the growing population and the need to address various challenges in the agricultural sector. Cloud computing and internet of things (IoT) technologies are playing a crucial role in the market. Farmers can now collect vast amounts of data from various sources, including weather data analytics, crop growth monitoring, land preparation, and farm operations. This data is then analyzed using predictive models to optimize farm output and improve efficiency.

Moreover, positioning systems and navigation satellite systems, such as those used in drones, are also contributing to the market. These technologies enable farmers to monitor their crops and livestock with precision, ensuring optimal growth conditions and preventing potential losses. Crop management is a significant application area for agriculture analytics. By analyzing data on soil degradation, climatic conditions, and crop growth patterns, farmers can make informed decisions on land preparation, irrigation, and fertilizer application. This results in improved crop yields and reduced waste. Livestock farming is another area where agriculture analytics is making a significant impact. Livestock analytics provides farmers with insights into animal health and productivity, enabling them to optimize feeding and breeding programs.

Also, this leads to increased profitability and improved animal welfare. Aquaculture analytics is another emerging application area in the market. By monitoring water quality, fish behavior, and environmental conditions, farmers can optimize fish farming operations and reduce the risk of disease outbreaks. The market offers both cloud deployment and on-premises deployment options. Cloud deployment provides farmers with the flexibility to access data and insights from anywhere, while on-premises deployment offers greater control over data security. Data Security is a critical concern in the market. Farmers must ensure that their data is protected from unauthorized access and cyber threats.

Additionally, advanced security measures, such as encryption and access controls, are essential to safeguard sensitive agricultural data. In conclusion, the market is transforming the agricultural sector by providing farmers and agricultural businesses with data-driven insights to optimize their operations, increase productivity, and improve efficiency. The market is driven by the growing demand for food production and the need to address various challenges in the agricultural sector, including climatic conditions, soil degradation, and positioning systems. By leveraging cloud computing, IoT, and advanced analytics techniques, farmers can make informed decisions and improve their bottom line.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Solutions
  Services


Application

  Precision farming
  Livestock monitoring
  Aquaculture farming
  Vertical farming
  Others


Geography

  North America

    Canada
    US


  Europe

    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Type Insights

The solutions segment is estimated to witness significant growth during the fore

Data and insights on the Agriculture sector in India - production and yield per hectare, minimum support price (MSP), and comparison with global peers.

In 2023, almost half of India’s GDP was generated by the services sector, a slight and steady increase over the last 10 years. Among the leading services industries in the country are telecommunications, IT, and software. The IT factorThe IT industry is a vital part of India’s economy, and in the fiscal year of 2016/2017, it generated about 8 percent of India’s GDP alone – a slight decrease from previous years, when it made up about 10 percent of the country’s economy. Nevertheless, the IT industry is growing, as is evident by its quickly increasing revenue and employment figures. IT includes software development, consulting, software management, and online services, and business process management (BPM). Employee migrationAlthough employment figures in IT, and thus in the services sector, are on the rise, most of the Indian workforce is still employed in agriculture, however, the figures show a trend pointing towards a reversal of this distribution. For now, the majority of Indians still do not live in cities – where IT jobs are generated – but urbanization is on the rise as well.

As per world agriculture statistics India is the world's largest producer of many fresh fruits like banana, mango, guava, papaya, lemon and vegetables like chickpea, okra and milk, major spices like chili pepper, ginger, fibrous crops such as jute, staples such as millets and castor oil seed. India is the second largest producer of wheat and rice, the world's major food staples.

India is currently the world's second largest producer of several dry fruits, agriculture-based textile raw materials, roots and tuber crops, pulses, farmed fish, eggs, coconut, sugarcane and numerous vegetables. India is ranked under the world's five largest producers of over 80% of agricultural produce items, including many cash crops such as coffee and cotton, in 2010. India is one of the world's five largest producers of livestock and poultry meat, with one of the fastest growth rates, as of 2011.

One report from 2008 claimed that India's population is growing faster than its ability to produce rice and wheat.[20] While other recent studies claim that India can easily feed its growing population, plus produce wheat and rice for global exports, if it can reduce food staple spoilage/wastage, improve its infrastructure and raise its farm productivity like those achieved by other developing countries such as Brazil and China.

Data collected from Ministry of Agriculture and Farmers Welfare of India

India Real Gross Capital Formation Growth: Agriculture, Forestry and Fishing data was reported at 2.210 % in 2018. This records a decrease from the previous number of 12.702 % for 2017. India Real Gross Capital Formation Growth: Agriculture, Forestry and Fishing data is updated yearly, averaging 4.667 % from Mar 1952 (Median) to 2018, with 67 observations. The data reached an all-time high of 49.632 % in 1991 and a record low of -30.394 % in 1992. India Real Gross Capital Formation Growth: Agriculture, Forestry and Fishing data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Agriculture Sector – Table IN.RIU002: Memo Items: Investment of Agriculture Sector. Data prior to 2013 is 2004-2005 base

Dataset Description

This dataset is sourced from FAOSTAT, the comprehensive statistical database maintained by the Food and Agriculture Organization (FAO) of the United Nations. It provides detailed and reliable data on global agriculture, food security, nutrition, and related topics. The dataset covers the period from 1971 to 2022, offering a 50-year perspective on trends and changes in agricultural production, trade, resource use, and environmental impacts.

Visit the FAOSTAT website: https://www.fao.org/faostat/.

Variables :

• Year: The year for which the data is recorded (e.g., 1971, 2022).
• China: A metric (likely percentage change or growth rate) for China in the given year.
• India: A metric for India in the given year.
• Pakistan: A metric for Pakistan in the given year.
• United Arab Emirates: A metric for the UAE in the given year.
• United Kingdom: A metric for the UK in the given year.
• United States of America: A metric for the USA in the given year.

Each column (except Year) represents a country and contains numerical values, possibly indicating growth rates, percentage changes, or other metrics over time.

Possible Sources International Organizations: FAOSTAT (Food and Agriculture Organization): Provides data on agriculture, food security, and related metrics. World Bank: Offers economic, demographic, and environmental data. United Nations (UN): Publishes data on global development indicators. IMF (International Monetary Fund): Provides financial and economic data. Government Agencies: National statistical offices (e.g., Census Bureau, Ministry of Agriculture). Central banks or economic departments. Research Institutions: Universities or think tanks that collect and analyze data for specific studies

Snapshot img

AI In Agriculture Market Size 2025-2029

The AI in agriculture market size is valued to increase by USD 3.09 billion, at a CAGR of 20.4% from 2024 to 2029. Imperative for increased food production and yield enhancement will drive the AI in agriculture market.

Major Market Trends & Insights

North America dominated the market and accounted for a 34% growth during the forecast period.
By Component - Software segment was valued at USD 427.90 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 431.68 million
Market Future Opportunities: USD 3090.40 million
CAGR from 2024 to 2029 : 20.4%

Market Summary

The agriculture industry is increasingly embracing artificial intelligence (AI) to enhance food production and improve yields, driven by the pressing need to feed a growing global population. This shift is marked by the acceleration towards full autonomy and specialized robotics in farming. AI solutions enable real-time monitoring of crop health, predictive analysis of weather patterns, and automated irrigation systems, resulting in significant efficiency gains. For instance, a leading agricultural technology company implemented an AI-powered system that optimized irrigation, reducing water usage by 20% and increasing crop yield by 15%. Precision agriculture techniques, including GPS technology in planting and spraying, are becoming increasingly popular for water conservation and optimizing resource usage.
However, the high cost of implementation and uncertain return on investment pose challenges to the widespread adoption of AI in agriculture. Despite these hurdles, the potential benefits, including increased operational efficiency, compliance with environmental regulations, and improved crop quality, make AI a compelling investment for forward-thinking farmers and agribusinesses.

What will be the Size of the AI In Agriculture Market during the forecast period?

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How is the AI In Agriculture Market Segmented ?

The AI in agriculture industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Hardware
  Services


Technology

  Machine learning
  Predictive analytics
  Computer vision


Application

  Precision farming
  Drone analytics
  Livestock monitoring
  Agriculture robotics
  Labor Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    The Netherlands
    UK


  APAC

    Australia
    China
    India
    Japan


  Rest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth, with the software segment leading the charge. This segment encompasses platforms, applications, and algorithms that convert raw data into valuable insights for farmers. Machine learning algorithms, a critical component of this segment, are revolutionizing agriculture through predictive analytics. Farmers can now forecast crop yields, anticipate disease outbreaks, and optimize harvest times using historical and real-time data. Deep learning, a more advanced subset of machine learning, powers computer vision applications, enabling weed detection, crop health monitoring, and precision spraying. The market is further bolstered by the integration of IoT in agriculture, drone-based surveillance, soil moisture sensors, and GPS-guided machinery.

A notable example of the market's potential is the reduction of water usage efficiency by up to 20% through irrigation scheduling systems and precision farming techniques. The future of agriculture lies in data-driven decision making, with the continuous deployment of sensor networks, predictive maintenance models, and livestock monitoring systems.

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The Software segment was valued at USD 427.90 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 34% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market is experiencing significant growth and transformation, with North America leading the charge. This region, comprising the United States and Canada, is home to large-scale farming operations that require operational efficiency gains and cost reductions to remain competitive. High labor cost

The data refers to trend in growth of number of pump sets energized in India. It also includes year on year wise Pump Sets energized and Cumulative pump sets energized in India.

According to Cognitive Market Research, the Global Farm Management Software Market is expected to have a market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.

The North American region is expected to have the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
The Europe region is the fastest growing region with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
Precision Farming has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period
Cloud Based segment has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.
The software segment has the largest market share with an expected market size of XX million in 2024 with a growing CAGR of XX% during the forecast period.

Market Dynamics

Key Drivers

An Increase in the Use of Artificial Intelligence, Machine Learning, and Learning for Real-Time Farm Data Management

The demand for real-time data for decision-making and the rise in agricultural activity has led to an expansion of the farm management software market. Artificial intelligence and machine learning are rapidly becoming popular in several farming applications, including fish farming, precision farming, sophisticated greenhouse techniques, and animal monitoring. The 'Saagu Baagu' pilot, for instance, was created in collaboration with the Telangana state government in its Khammam district, supported by the Bill and Melinda Gates Foundation, and carried out by Digital Green in India, is reportedly one of the most successful implementations of the AI4AI initiative, according to the World Economic Forum. For over 7,000 farmers, the project has significantly enhanced the value chain for chilies. Telangana's state government, which established the nation's first framework for Agri data management and exchange as well as other supportive policies, has been instrumental in this shift. Saagu Baagu has shown impressive outcomes during its initial period of operation. A 21% increase in chili yields per acre, a 9% decrease in pesticide use, a 5% decrease in fertilizer use, and an 8% increase in unit prices as a result of quality improvements were observed by farmers involved in the program. Farmers' revenues have increased by more than INR 66,000 (about 800 USD) per acre per crop cycle as a result of these changes, nearly doubling their income. To make farm management easier, farm management software controls the data flow between hardware and personnel. Understanding the environment through data analysis from several farm management instruments, such as GPS, satellite imagery, and in-field sensors, is the main objective of the farm management framework. Data management is essential since agricultural management decisions are based on real-time data analysis from farm activities. With the increasing prevalence of artificial intelligence and machine learning, data management functions like as planning, purchasing, feeding, harvesting, marketing, and inventory control can now be facilitated by real-time access to data. The collection of real-time data from farming operations facilitates analysis and decision-making, thereby favoring the market growth for the farm management software market. (Source- https://www.weforum.org/impact/ai-for-agriculture-in-india/)

The implementation of government policies

The implementation of policies by governments across various countries is expected to facilitate the adoption of advanced agricultural techniques, hence driving the global market for farm management software. Adoption of precision agriculture, research and development, instruction, and training are supported by federal authorities. USDA offers financing programs and financial aid to encourage the adoption of precision agriculture technologies. One such program pays farmers for adopting practices that have a positive impact on conservation. For the fiscal years 2017–2021, the National Science Foundation (NSF) and the USDA will jointly grant about $200 million for precision agricultural research and development. The two agencies' collaborations to assist artificial intelligence (AI) research institutes are part of this funding. Similarly, the Indian go...

Data Monetization in Agriculture Market Outlook

The global data monetization in agriculture market size reached USD 3.2 billion in 2024, with a robust growth trajectory reflected in its CAGR of 15.7% from 2025 to 2033, as per our latest research. By 2033, the market is forecasted to reach USD 10.3 billion, driven by the increasing adoption of digital technologies, the proliferation of IoT sensors, and the mounting need for data-driven decision-making across agriculture value chains. The surge in market size is primarily attributed to the growing recognition among stakeholders that agricultural data, when effectively harnessed and commercialized, can unlock significant value, enhance productivity, and drive sustainability.

One of the primary growth factors fueling the data monetization in agriculture market is the exponential increase in data generation across farms globally. The widespread deployment of IoT devices, such as soil sensors, weather stations, and drone-based imaging systems, is generating vast volumes of actionable data. This data, when aggregated, analyzed, and shared via robust platforms, can be monetized by providing insights to various stakeholders, including farmers, agribusinesses, and third-party service providers. The integration of advanced analytics and artificial intelligence (AI) further amplifies the value of this data, enabling precision agriculture practices that optimize input usage, improve yields, and reduce environmental impact. As a result, both data providers and consumers are increasingly recognizing the commercial potential of agricultural data assets.

Another significant driver is the rise of platform-based ecosystems that facilitate seamless data exchange and monetization. Several agritech startups and established technology companies are building data marketplaces where raw and processed agricultural data can be bought, sold, or licensed. These platforms offer secure, transparent, and scalable mechanisms for data sharing while ensuring compliance with privacy and ownership regulations. The emergence of such marketplaces not only democratizes access to critical information but also creates new revenue streams for data owners, including smallholder farmers and cooperatives. Additionally, the growing emphasis on supply chain transparency and traceability has heightened the demand for real-time data, further boosting the monetization potential across the agricultural sector.

The evolving regulatory landscape and increasing government support for digital agriculture initiatives are also pivotal in shaping the growth of the data monetization in agriculture market. Policymakers in major agricultural economies are implementing frameworks that encourage data sharing while safeguarding farmer interests and ensuring data privacy. Public-private partnerships are being established to promote open data standards, interoperability, and digital infrastructure investments. These efforts are fostering a conducive environment for data-driven innovation, attracting investments, and accelerating the adoption of monetization models across the agricultural value chain. As more regions embrace digital transformation in agriculture, the market's growth momentum is expected to remain strong in the coming years.

From a regional perspective, North America currently leads the global data monetization in agriculture market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America is underpinned by the early adoption of precision agriculture technologies, a highly digitized farming landscape, and the presence of leading agritech companies. Europe is witnessing rapid growth due to stringent sustainability regulations and increasing investments in smart farming solutions. Meanwhile, Asia Pacific is emerging as a high-growth region, driven by the digitalization of agriculture in countries such as China, India, and Australia. These regions collectively contribute to the global expansion of data monetization opportunities in agriculture, each with unique growth drivers and challenges.

Component Analysis

The component segment of the data monetization in agriculture market is primarily bifurcated into tools and services, each playing a distinct yet complementary role in enabling the commercialization of agricultural data. Tools encompass a broad spectrum of software platforms, analytics engines, data integration modules, and visualization d