Big Data Analytics in Agriculture Market: Segmented By Application (Farm Analytics, Livestock Analytics, and Aquaculture Analytics); By Component (Solution and Services) and Region – Global Analysis of Market Size, Share & Trends for 2019–2020 and Forecasts to 2030
[162+ Pages Research Report] Global Big Data Analytics in Agriculture Market to surpass USD 1.8 billion by 2030 from USD 0.89 billion in 2020 at a CAGR of 13.65 % in the coming years, i.e., 2021-30.
Product Overview
The compilation of vast and complicated data series that are typically difficult to process using standard database management techniques is known as big data analytics and processing. In almost every region of the world, the agriculture sector is undergoing policy reforms. As a result, new production method projects such as contract farming or corporate farming are being tried. Big data is a term that describes a vast amount of data that is mined for information that can assist an organization in making informed decisions and strategic business steps. In agricultural applications, big data is becoming increasingly important to help farmers face the challenges of a rising global population, climate change, and urbanization.
Market Highlights
Global Big Data Analytics in the Agriculture market is expected to project a notable CAGR of 13.65% in 2030.
Global Big Data Analytics in Agriculture to surpass USD 1.8 billion by 2030 from USD 0.89 billion in 2020 at a CAGR of 13.65 % in the coming years, i.e., 2021-30. The main driving forces behind the growth of Big Data Analytics in Agriculture include increased agricultural mechanisms in developing countries, rising labour costs as a result of a lack of skilled labour, increased pressure for food supplies in the world as a result of increasing population, significant savings on smart farming techniques and government initiatives to adopt advanced agricultural technology. The increasing need for an optimal crop production with scarce funds gives it tremendous popularity among farmers.
Global Big Data Analytics in Agriculture Market: Segments
Livestock Analytics segment to grow with the highest CAGR during 2020-30
Global Big Data Analytics in Agriculture market is segmented by Application into Farm Analytics, Livestock Analytics, and Aquaculture Analytics. The area of application for livestock analytics includes feed management, heat stress management, race management, and management of the behavior, and other areas. Whereas others include the management of calves, genetics, and animals. The farm includes a variety of daily tasks, resulting in large numbers of critical animal data. Farmers could suffer major losses from any mismanagement in the diet or animal tracking. Consequently, farmers invest in the management of their livestock processes to produce quality analytics solutions.
Solution segment to grow with the highest CAGR during 2020-30
Global Big Data Analytics in Agriculture is divided by component into Solution and Services. The growing need for agriculture to efficiently evaluate critical data relating to improved decision-making is contributing to the growth of the agricultural analytics solution segment. The output of crops depends on multiple factors like weather, soil condition, the application of fertilizers, and the variety of seeds. Farmers face great challenges in identifying essential areas from large data sets that can affect the productivity of their farms. The solution of farm analysis enables a broad range of data to be combined to gain valuable insight into increasing productivity.
Market Dynamics
Drivers
Increasing popularity of urban farming
In recent years, urban farming has become a popular trend worldwide, both in favor of the population who grow food in community parts or in roof gardens. While the capacity to supply urban populations with locally grown crops is not to be diminished, many advantages go beyond providing local communities with food. The effects of urban precipitation runoff reduction and the improvement of air quality is favorable on the environment. The work to plan and retain these parts also contributes to strengthening the social bonds of the community. Using large-scale data analysis and AI, urban farmers can enhance their efficiency and maximize their limited available space. Some researchers, consider that these efforts could produce up to 180 million metric tons of food each year, accounting for approximately one-tenth of the world's vegetable crop, legumes, and tubers.
Increase in overall productivity
In a herd of hundreds or thousands of cows, diseases can spread rapidly. In many cases, diseases contractual to dozens of other cows are spread to farmers before they realize the problem. Fortunately, there are several IoT gadgets to avoid this problem and others. Some evaluate fertility, which might be particularly useful on characteristics in which farmers are highly dependent on successful breeding. Others report to farmers when cows are in high milk production periods. On the basis of the information, farmers can add a type of grain to the feeding regimes of an animal.
Restraint
High costs
The biggest factor that restricts big data analytics 's in agriculture is the massive cost of accurate field data collection. In order to make farmers afraid of the utilization of this technology in the collection of precision field data, AI technology requires high initial investments, effective agricultural instruments, and qualified and knowledgeable farmers or producers among others. Farms or growers must make enormous investments in GPS, drones, and GIS for the collection of inputs from satellites and other GPS devices for variable rate application technology. AI-based agriculture is expensive, because it involves, for example, the collection of data for geospatial data (efficient input selection) and data management training.
Global Big Data Analytics in Agriculture Market: Key Players
Deere & Company
Company Overview, Business Strategy, Key Product Offerings, Financial Performance, Key Performance Indicators, Risk Analysis, Recent Development, Regional Presence, SWOT Analysis
Taranis
AgEagle
Teejet Technologies
The Climate Corporation
Trimble
AGCO Corporation
AgJunction
Raven Industries
AG Leader
Topcon Positioning Systems
Descartes Labs
ec2ce
Prospera Technologies
Autonomous Tractor Corporation
Other Prominent Players
Global Big Data Analytics in Agriculture Market: Regions
Global Big Data Analytics in Agriculture market is segmented based on regional analysis into five major regions. These include North America, Latin America, Europe, Asia Pacific, and the Middle East and Africa. Global Big Data Analytics in Agriculture in North America held the largest market share of XX.X% in the year 2020. In 2020, America accounted for the greatest share of the agricultural precision market. Countries like the United States and Canada in America are the first to implement Big Data Analytics in Agriculture, which is a key reason for this region's significant proportion of the precision agricultural market. The fastest-growing size in North America is expected to prevail between 2020 and 2025. The region mainly has large farmhouses with the best agricultural equipment, which contribute largely to the growth of the market in agricultural analytics. Due to increasing advancements in digital agriculture practices and the implementation of cloud-based alternatives among farmers in the area, APAC is anticipated to fuel the growth for vendors of agricultural analysis. However, the absence of data standards in different regions could influence the adoption of a solution in agriculture analysis
Global Big Data Analytics in Agriculture Market is further segmented by region into:
North America Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – United States and Canada
Latin America Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – Mexico, Argentina, Brazil, and Rest of Latin America
Europe Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – United Kingdom, France, Germany, Italy, Spain, Belgium, Hungary, Luxembourg, Netherlands, Poland, NORDIC, Russia, Turkey, and Rest of Europe
Asia Pacific Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – India, China, South Korea, Japan, Malaysia, Indonesia, New Zealand, Australia, and Rest of APAC
Middle East and Africa Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – North Africa, Israel, GCC, South Africa, and Rest of MENA
Global Big Data Analytics in Agriculture report also contains analysis on:
Big Data Analytics in Agriculture Segments:
By Component
Solution
Services
By Application
Farm Analytics
Livestock Analytics
Aquaculture Analytics
Big Data Analytics in Agriculture Market Dynamics
Big Data Analytics in Agriculture Market Size
Supply & Demand
Current Trends/Issues/Challenges
Competition & Companies Involved in the Market
Value Chain of the Market
Market Drivers and Restraints
Big Data Analytics in Agriculture Market Report Scope and Segmentation
Frequently Asked Questions
How big is Big Data Analytics in the Agriculture market?
What is the Big Data Analytics in Agriculture market growth?
Which segment accounted for the largest Big Data Analytics in Agriculture market share?
Who are the key players in Big Data Analytics in the Agriculture market?
What are the factors driving the Big Data Analytics in the Agriculture market?
Product Overview
The compilation of vast and complicated data series that are typically difficult to process using standard database management techniques is known as big data analytics and processing. In almost every region of the world, the agriculture sector is undergoing policy reforms. As a result, new production method projects such as contract farming or corporate farming are being tried. Big data is a term that describes a vast amount of data that is mined for information that can assist an organization in making informed decisions and strategic business steps. In agricultural applications, big data is becoming increasingly important to help farmers face the challenges of a rising global population, climate change, and urbanization.
Market Highlights
Global Big Data Analytics in the Agriculture market is expected to project a notable CAGR of 13.65% in 2030.
Global Big Data Analytics in Agriculture to surpass USD 1.8 billion by 2030 from USD 0.89 billion in 2020 at a CAGR of 13.65 % in the coming years, i.e., 2021-30. The main driving forces behind the growth of Big Data Analytics in Agriculture include increased agricultural mechanisms in developing countries, rising labour costs as a result of a lack of skilled labour, increased pressure for food supplies in the world as a result of increasing population, significant savings on smart farming techniques and government initiatives to adopt advanced agricultural technology. The increasing need for an optimal crop production with scarce funds gives it tremendous popularity among farmers.
Global Big Data Analytics in Agriculture Market: Segments
Livestock Analytics segment to grow with the highest CAGR during 2020-30
Global Big Data Analytics in Agriculture market is segmented by Application into Farm Analytics, Livestock Analytics, and Aquaculture Analytics. The area of application for livestock analytics includes feed management, heat stress management, race management, and management of the behavior, and other areas. Whereas others include the management of calves, genetics, and animals. The farm includes a variety of daily tasks, resulting in large numbers of critical animal data. Farmers could suffer major losses from any mismanagement in the diet or animal tracking. Consequently, farmers invest in the management of their livestock processes to produce quality analytics solutions.
Solution segment to grow with the highest CAGR during 2020-30
Global Big Data Analytics in Agriculture is divided by component into Solution and Services. The growing need for agriculture to efficiently evaluate critical data relating to improved decision-making is contributing to the growth of the agricultural analytics solution segment. The output of crops depends on multiple factors like weather, soil condition, the application of fertilizers, and the variety of seeds. Farmers face great challenges in identifying essential areas from large data sets that can affect the productivity of their farms. The solution of farm analysis enables a broad range of data to be combined to gain valuable insight into increasing productivity.
Market Dynamics
Drivers
Increasing popularity of urban farming
In recent years, urban farming has become a popular trend worldwide, both in favor of the population who grow food in community parts or in roof gardens. While the capacity to supply urban populations with locally grown crops is not to be diminished, many advantages go beyond providing local communities with food. The effects of urban precipitation runoff reduction and the improvement of air quality is favorable on the environment. The work to plan and retain these parts also contributes to strengthening the social bonds of the community. Using large-scale data analysis and AI, urban farmers can enhance their efficiency and maximize their limited available space. Some researchers, consider that these efforts could produce up to 180 million metric tons of food each year, accounting for approximately one-tenth of the world's vegetable crop, legumes, and tubers.
Increase in overall productivity
In a herd of hundreds or thousands of cows, diseases can spread rapidly. In many cases, diseases contractual to dozens of other cows are spread to farmers before they realize the problem. Fortunately, there are several IoT gadgets to avoid this problem and others. Some evaluate fertility, which might be particularly useful on characteristics in which farmers are highly dependent on successful breeding. Others report to farmers when cows are in high milk production periods. On the basis of the information, farmers can add a type of grain to the feeding regimes of an animal.
Restraint
High costs
The biggest factor that restricts big data analytics 's in agriculture is the massive cost of accurate field data collection. In order to make farmers afraid of the utilization of this technology in the collection of precision field data, AI technology requires high initial investments, effective agricultural instruments, and qualified and knowledgeable farmers or producers among others. Farms or growers must make enormous investments in GPS, drones, and GIS for the collection of inputs from satellites and other GPS devices for variable rate application technology. AI-based agriculture is expensive, because it involves, for example, the collection of data for geospatial data (efficient input selection) and data management training.
Global Big Data Analytics in Agriculture Market: Key Players
Deere & Company
Company Overview, Business Strategy, Key Product Offerings, Financial Performance, Key Performance Indicators, Risk Analysis, Recent Development, Regional Presence, SWOT Analysis
Taranis
AgEagle
Teejet Technologies
The Climate Corporation
Trimble
AGCO Corporation
AgJunction
Raven Industries
AG Leader
Topcon Positioning Systems
Descartes Labs
ec2ce
Prospera Technologies
Autonomous Tractor Corporation
Other Prominent Players
Global Big Data Analytics in Agriculture Market: Regions
Global Big Data Analytics in Agriculture market is segmented based on regional analysis into five major regions. These include North America, Latin America, Europe, Asia Pacific, and the Middle East and Africa. Global Big Data Analytics in Agriculture in North America held the largest market share of XX.X% in the year 2020. In 2020, America accounted for the greatest share of the agricultural precision market. Countries like the United States and Canada in America are the first to implement Big Data Analytics in Agriculture, which is a key reason for this region's significant proportion of the precision agricultural market. The fastest-growing size in North America is expected to prevail between 2020 and 2025. The region mainly has large farmhouses with the best agricultural equipment, which contribute largely to the growth of the market in agricultural analytics. Due to increasing advancements in digital agriculture practices and the implementation of cloud-based alternatives among farmers in the area, APAC is anticipated to fuel the growth for vendors of agricultural analysis. However, the absence of data standards in different regions could influence the adoption of a solution in agriculture analysis
Global Big Data Analytics in Agriculture Market is further segmented by region into:
North America Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – United States and Canada
Latin America Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – Mexico, Argentina, Brazil, and Rest of Latin America
Europe Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – United Kingdom, France, Germany, Italy, Spain, Belgium, Hungary, Luxembourg, Netherlands, Poland, NORDIC, Russia, Turkey, and Rest of Europe
Asia Pacific Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – India, China, South Korea, Japan, Malaysia, Indonesia, New Zealand, Australia, and Rest of APAC
Middle East and Africa Market Size, Share, Trends, Opportunities, Y-o-Y Growth, CAGR – North Africa, Israel, GCC, South Africa, and Rest of MENA
Global Big Data Analytics in Agriculture report also contains analysis on:
Big Data Analytics in Agriculture Segments:
By Component
Solution
Services
By Application
Farm Analytics
Livestock Analytics
Aquaculture Analytics
Big Data Analytics in Agriculture Market Dynamics
Big Data Analytics in Agriculture Market Size
Supply & Demand
Current Trends/Issues/Challenges
Competition & Companies Involved in the Market
Value Chain of the Market
Market Drivers and Restraints
Big Data Analytics in Agriculture Market Report Scope and Segmentation
Frequently Asked Questions
How big is Big Data Analytics in the Agriculture market?
What is the Big Data Analytics in Agriculture market growth?
Which segment accounted for the largest Big Data Analytics in Agriculture market share?
Who are the key players in Big Data Analytics in the Agriculture market?
What are the factors driving the Big Data Analytics in the Agriculture market?
1. EXECUTIVE SUMMARY
2. GLOBAL BIG DATA ANALYTICS IN AGRICULTURE MARKET
2.1. Product Overview
2.2. Market Definition
2.3. Segmentation
2.4. Assumptions and Acronyms
3. RESEARCH METHODOLOGY
3.1. Research Objectives
3.2. Primary Research
3.3. Secondary Research
3.4. Forecast Model
3.5. Market Size Estimation
4. AVERAGE PRICING ANALYSIS
5. MACRO-ECONOMIC INDICATORS
6. MARKET DYNAMICS
6.1. Growth Drivers
6.2. Restraints
6.3. Opportunity
6.4. Trends
7. CORRELATION & REGRESSION ANALYSIS
7.1. Correlation Matrix
7.2. Regression Matrix
8. RECENT DEVELOPMENT, POLICIES & REGULATORY LANDSCAPE
9. RISK ANALYSIS
9.1. Demand Risk Analysis
9.2. Supply Risk Analysis
10. GLOBAL BIG DATA ANALYTICS IN AGRICULTURE MARKET ANALYSIS
10.1. Porters Five Forces
10.1.1. Threat of New Entrants
10.1.2. Bargaining Power of Suppliers
10.1.3. Threat of Substitutes
10.1.4. Rivalry
10.2. PEST Analysis
10.2.1. Political
10.2.2. Economic
10.2.3. Social
10.2.4. Technological
11. GLOBAL BIG DATA ANALYTICS IN AGRICULTURE MARKET
11.1. Market Size & forecast, 2020A-2030F
11.1.1. By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
11.1.2. By Volume (Billion Units) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12. GLOBAL BIG DATA ANALYTICS IN AGRICULTURE MARKET: MARKET SEGMENTATION
12.1. By Regions
12.1.1. North America:(U.S. and Canada), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.1.2. Latin America: (Brazil, Mexico, Argentina, Rest of Latin America), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.1.3. Europe: (Germany, UK, France, Italy, Spain, BENELUX, NORDIC, Hungary, Poland, Turkey, Russia, Rest of Europe), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.1.4. Asia-Pacific: (China, India, Japan, South Korea, Indonesia, Malaysia, Australia, New Zealand, Rest of Asia Pacific), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.1.5. Middle East and Africa: (Israel, GCC, North Africa, South Africa, Rest of Middle East and Africa), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.2. By Application: Market Share (2020-2030F)
12.2.1. Farm Analytics, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.2.2. Livestock Analytics, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.2.3. Aquaculture Analytics, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.3. By Component: Market Share (2020-2030F)
12.3.1. Solution, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.3.2. Services, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
Company Profile
1. DEERE & COMPANY (US)
1. COMPANY OVERVIEW
2. COMPANY TOTAL REVENUE (FINANCIALS)
3. MARKET POTENTIAL
4. GLOBAL PRESENCE
5. KEY PERFORMANCE INDICATORS
6. SWOT ANALYSIS
7. PRODUCT LAUNCH
2. TARANIS (ISRAEL)
3. AGEAGLE (US)
4. TEEJET TECHNOLOGIES (US)
5. THE CLIMATE CORPORATION (US)
6. TRIMBLE (US)
7. AGCO CORPORATION (US)
8. AGJUNCTION (US)
9. RAVEN INDUSTRIES (US)
10. AG LEADER (US
11. TOPCON POSITIONING SYSTEMS (US)
12. DESCARTES LABS (US)
13. EC2CE (SPAIN)
14. PROSPERA TECHNOLOGIES (ISRAEL)
15. AUTONOMOUS TRACTOR CORPORATION (US)
16. OTHER PROMINENT PLAYERS
Consultant Recommendation
**The above-given segmentations and companies could be subjected to further modification based on in-depth feasibility studies conducted for the final deliverable.
2. GLOBAL BIG DATA ANALYTICS IN AGRICULTURE MARKET
2.1. Product Overview
2.2. Market Definition
2.3. Segmentation
2.4. Assumptions and Acronyms
3. RESEARCH METHODOLOGY
3.1. Research Objectives
3.2. Primary Research
3.3. Secondary Research
3.4. Forecast Model
3.5. Market Size Estimation
4. AVERAGE PRICING ANALYSIS
5. MACRO-ECONOMIC INDICATORS
6. MARKET DYNAMICS
6.1. Growth Drivers
6.2. Restraints
6.3. Opportunity
6.4. Trends
7. CORRELATION & REGRESSION ANALYSIS
7.1. Correlation Matrix
7.2. Regression Matrix
8. RECENT DEVELOPMENT, POLICIES & REGULATORY LANDSCAPE
9. RISK ANALYSIS
9.1. Demand Risk Analysis
9.2. Supply Risk Analysis
10. GLOBAL BIG DATA ANALYTICS IN AGRICULTURE MARKET ANALYSIS
10.1. Porters Five Forces
10.1.1. Threat of New Entrants
10.1.2. Bargaining Power of Suppliers
10.1.3. Threat of Substitutes
10.1.4. Rivalry
10.2. PEST Analysis
10.2.1. Political
10.2.2. Economic
10.2.3. Social
10.2.4. Technological
11. GLOBAL BIG DATA ANALYTICS IN AGRICULTURE MARKET
11.1. Market Size & forecast, 2020A-2030F
11.1.1. By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
11.1.2. By Volume (Billion Units) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12. GLOBAL BIG DATA ANALYTICS IN AGRICULTURE MARKET: MARKET SEGMENTATION
12.1. By Regions
12.1.1. North America:(U.S. and Canada), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.1.2. Latin America: (Brazil, Mexico, Argentina, Rest of Latin America), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.1.3. Europe: (Germany, UK, France, Italy, Spain, BENELUX, NORDIC, Hungary, Poland, Turkey, Russia, Rest of Europe), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.1.4. Asia-Pacific: (China, India, Japan, South Korea, Indonesia, Malaysia, Australia, New Zealand, Rest of Asia Pacific), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.1.5. Middle East and Africa: (Israel, GCC, North Africa, South Africa, Rest of Middle East and Africa), By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.2. By Application: Market Share (2020-2030F)
12.2.1. Farm Analytics, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.2.2. Livestock Analytics, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.2.3. Aquaculture Analytics, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.3. By Component: Market Share (2020-2030F)
12.3.1. Solution, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
12.3.2. Services, By Value (USD Billion) 2020-2030F; Y-o-Y Growth (%) 2021-2030F
Company Profile
1. DEERE & COMPANY (US)
1. COMPANY OVERVIEW
2. COMPANY TOTAL REVENUE (FINANCIALS)
3. MARKET POTENTIAL
4. GLOBAL PRESENCE
5. KEY PERFORMANCE INDICATORS
6. SWOT ANALYSIS
7. PRODUCT LAUNCH
2. TARANIS (ISRAEL)
3. AGEAGLE (US)
4. TEEJET TECHNOLOGIES (US)
5. THE CLIMATE CORPORATION (US)
6. TRIMBLE (US)
7. AGCO CORPORATION (US)
8. AGJUNCTION (US)
9. RAVEN INDUSTRIES (US)
10. AG LEADER (US
11. TOPCON POSITIONING SYSTEMS (US)
12. DESCARTES LABS (US)
13. EC2CE (SPAIN)
14. PROSPERA TECHNOLOGIES (ISRAEL)
15. AUTONOMOUS TRACTOR CORPORATION (US)
16. OTHER PROMINENT PLAYERS
Consultant Recommendation
**The above-given segmentations and companies could be subjected to further modification based on in-depth feasibility studies conducted for the final deliverable.
