農業分析市場報告：2031 年趨勢、預測與競爭分析

全球農業分析市場的未來充滿希望，為農民、農學家、農業企業、農業研究人員和政府組織提供了機會。預計到 2031 年，全球農業分析市場規模將達到 37 億美元，2025 年至 2031 年的複合年成長率為 12.7%。該市場的主要驅動力是對最佳化資源利用的需求不斷增加、政府措施改進農業技術的舉措不斷增多以及物聯網設備的採用率不斷提高。

• Lucintel 預測，根據農業類型，畜牧業將在預測期內經歷最高的成長。
• 根據最終用途，農學家預計將經歷最高的成長。
• 從地區來看，由於農業產量的成長和對數位農業技術的投資增加，預計北美在預測期內仍將是最大的地區。

農業分析市場的策略性成長機會

資料驅動技術在各種應用中的日益普及極大地推動了農業分析市場的發展。隨著農業實踐不斷發展以滿足全球糧食需求和永續性目標，出現了一些策略性成長機會。這些機會涵蓋了農業分析的一系列應用，並提供了創新和擴展的潛力。透過專注於關鍵應用，公司和相關人員可以利用這些機會來提高農業領域的生產力、效率和永續性。

• 精密農業：精密農業是農業分析市場的一個重要的成長機會，這得益於 GPS 技術、感測器和資料分析的進步。該應用程式可以精確管理水、肥料和農藥等作物投入，以適應特定的田間條件。透過最佳化資源利用和減少浪費，精密農業可以提高作物產量並減少對環境的影響。對永續農業方法和更有效率的資源管理需求不斷成長，推動著精密農業解決方案的擴展。
• 作物管理和產量預測：作物管理和產量預測的高級分析是一個主要的成長領域。透過利用衛星影像、天氣預報和歷史作物生長資料，這些工具可以準確預測作物產量和健康狀況。這使農民能夠就種植、收穫和資源分配做出明智的決定。預測作物產量的能力有助於最佳化農業規劃，降低與產量變化相關的風險，並支持整體農場生產力和盈利。
• 土壤健康監測：土壤健康監測作為農業分析的重要應用，正越來越受歡迎。分析土壤特性（例如養分水平、水分含量和 pH 值）的技術為土壤管理提供了寶貴的見解。此應用可幫助農民維持最佳土壤條件，改善作物生長並長期維持土壤肥力。人們越來越意識到土壤健康對農業永續性的影響，導致對土壤監測解決方案的投資增加，為該產業創造了巨大的成長機會。
• 牲畜監測：牲畜監測是農業分析的新興應用，專注於改善牲畜健康和生產力。使用穿戴式感測器、GPS 追蹤和資料分析的解決方案可提供有關動物行為、健康和生產力的即時洞察。該應用程式使農民能夠及早發現問題，最佳化飼養方法並加強整體牛管理。對動物福利和高效牲畜管理的日益重視推動了對先進牲畜監測技術的需求。
• 供應鏈最佳化：供應鏈最佳化是農業分析領域的策略成長機會。追蹤和分析農產品從農場到餐桌的流動過程的資料分析平台可以幫助簡化業務、減少浪費並提高可追溯性。透過加強供應鏈管理，這些解決方案可支持更好的決策並有助於建立更有效率、更永續的農業供應鏈。

農業分析的策略性成長機會將透過提高精度、效率和永續性來塑造農業部門的未來。精密農業、作物管理、土壤健康監測、牲畜監測和供應鏈最佳化的應用推動了創新和成長。隨著這些機會的不斷發展，預計它們將對農業實踐產生重大影響，有助於提高生產力並實現更永續的農業方法。

農業分析市場促進因素與挑戰

農業分析市場受到各種促進因素​​和挑戰的影響，這些因素和挑戰決定了其發展。技術進步、經濟因素和監管考量在推動市場成長和解決障礙方面發揮關鍵作用。了解這些促進因素和挑戰對於相關人員有效駕馭市場、利用成長機會並降低風險至關重要。

農業分析市場受以下因素驅動：

• 技術進步：感測器、資料分析和人工智慧技術的快速進步將推動農業分析的發展。用於土壤和作物監測的先進感測器、用於預測分析的機器學習演算法以及用於航空攝影的無人機等技術創新將增強資料收集和分析。這些技術實現了精密農業，改善了資源管理並最佳化了作物產量。技術不斷發展，提供新的能力和效率，推動農業分析市場向前發展。
• 不斷成長的糧食安全需求：全球人口的成長和糧食需求的增加是農業分析市場的主要驅動力。為了滿足這些需求，農業需要提高生產力和效率。農業分析提供工具和見解，幫助最佳化作物產量、有效管理資源並確保糧食安全。隨著生產更多糧食的壓力越來越大，採用資料主導的解決方案對於實現永續農業至關重要。
• 關注永續性：永續性是農業分析的關鍵驅動力，重點是減少環境影響和促進環境友善的農業實踐。支持精密農業、土壤健康監測和資源最佳化的分析解決方案有助於最大限度地減少廢棄物和節約資源，實現永續性目標。人們對環境問題的認知不斷提高，以及採用永續做法的監管壓力，正在推動對有助於農業部門更綠色的分析解決方案的需求。
• 政府支持和投資：政府在農業技術和資料分析方面的舉措和投資推動了市場成長。許多政府推出政策並提供資金支持農業採用先進技術。這些投資將加速農業分析解決方案的開發和部署，使農民更容易獲得這些解決方案並推動整體市場成長。
• 物聯網和巨量資料的採用日益增多：物聯網 (IoT) 設備和巨量資料分析的採用將推動農業創新。物聯網感測器提供農場營運各方面的即時資料，巨量資料分析平台則為決策提供見解。整合這些技術將使農民能夠更有效地監控和管理他們的營運，從而提高效率和生產力。物聯網和巨量資料的廣泛應用極大地促進了農業分析的發展。

農業分析市場面臨的挑戰是：

• 實施成本高：農業分析市場面臨的主要挑戰之一是先進技術的實施成本高。對於中小型農場來說，感測器、資料分析平台和其他技術所需的初始投資可能會過高。高昂的實施成本可能會限制農業分析和解決方案的採用，並成為一些相關人員的進入壁壘，影響整體市場的成長。
• 資料隱私和安全問題：資料隱私和安全問題是農業分析市場面臨的關鍵挑戰。農業分析涉及敏感資料的收集和分析，這意味著存在資料外洩和濫用的風險。確保強力的資料保護措施和解決隱私問題對於維護信任和推動分析解決方案的採用至關重要。嚴格的安全通訊協定的需求是市場持續面臨的挑戰。
• 開發中地區技術取得受限：新興市場對先進技術和基礎設施的取得受限對農業分析市場構成了挑戰。技術基礎設施薄弱的地區可能較慢採用資料主導的解決方案。縮小技術差距並提供經濟實惠、易於取得的解決方案對於提高農業分析的採用率並確保其效益在全球範圍內實現至關重要。

農業分析市場受到技術進步、糧食安全需求、永續性目標、政府支持以及物聯網和巨量資料採用的顯著影響。然而，實施成本高、資料隱私問題以及新興地區技術取得有限等挑戰也對市場產生了影響。解決這些促進因素和挑戰對於農業分析的持續發展和演變至關重要，使該行業能夠實現提高生產力和永續性的目標。

目錄

第1章執行摘要

第2章全球農業分析市場：市場動態

• 簡介、背景和分類
• 供應鏈
• 產業驅動力與挑戰

第3章 2019年至2031年市場趨勢及預測分析

• 宏觀經濟趨勢（2019-2024）及預測（2025-2031）
• 全球農業分析市場趨勢（2019-2024）與預測（2025-2031）
• 按農業類型分類的全球農業分析市場
  • 精密農業
  • 畜牧業
  • 水產養殖
  • 垂直農業
  • 其他
• 全球農業分析市場（按農場規模）
  • 大型農場
  • 中小型農場
• 全球農業分析市場（按技術）
  • 遙感探測和衛星影像
  • 地理資訊系統
  • 機器人與自動化
  • 巨量資料和雲端運算
  • 視覺化和報告
  • 區塊鏈技術
  • 其他
• 全球農業分析市場（按最終用途）
  • 農民
  • 農學家
  • 農業相關產業
  • 農業研究員
  • 政府
  • 其他

第4章2019年至2031年區域市場趨勢與預測分析

• 按地區分類：全球農業分析市場
• 北美農業分析市場
• 歐洲農業分析市場
• 亞太農業分析市場
• 世界其他地區農業分析市場

第5章 競爭分析

• 產品系列分析
• 業務整合
• 波特五力分析

第6章 成長機會與策略分析

• 成長機會分析
  • 全球農業分析市場按農業類型分類的成長機會
  • 全球農業分析市場成長機會（依農場規模）
  • 全球農業分析市場成長機會（按技術）
  • 全球農業分析市場成長機會（按最終用途分類）
  • 全球農業分析市場各區域成長機會
• 全球農業分析市場的新趨勢
• 戰略分析
  • 新產品開發
  • 擴大全球農業分析市場的容量
  • 全球農業分析市場的合併、收購和合資企業
  • 認證和許可

第7章主要企業簡介

• Deere & Company
• IBM
• Bayer
• SAP
• Trimble
• Accenture
• ABACO
• DeLaval
• Oracle
• DTN

The future of the global agriculture analytic market looks promising with opportunities in the farmer, agronomist, agribusiness, agricultural researcher, and government agency markets. The global agriculture analytic market is expected to reach an estimated $3.7 billion by 2031 with a CAGR of 12.7% from 2025 to 2031. The major drivers for this market are the growing need for optimal resource utilization, rising government initiatives for deploying improved agricultural techniques, and the rise in the adoption of IoT devices.

• Lucintel forecasts that, Within the agriculture type category, livestock farming is expected to witness the highest growth over the forecast period.
• Within the end use category, agronomist is expected to witness the highest growth.
• In terms of regions, North America will remain the largest region over the forecast period due to expanding the region's agricultural output and investing more in digital agriculture techniques.

Gain valuable insights for your business decisions with our comprehensive 150+ page report.

Emerging Trends in the Agriculture Analytic Market

The agriculture analytics market is witnessing several emerging trends that are reshaping how data is used in farming. These trends reflect advancements in technology and changing priorities within the agricultural sector, from enhancing precision to promoting sustainability. As technology continues to evolve, these trends drive significant transformations in how agricultural data is collected, analyzed, and applied, leading to more efficient and sustainable farming practices.

• AI and Machine Learning Integration: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into agriculture analytics, offering advanced predictive capabilities and automated decision-making. AI algorithms analyze vast amounts of data from various sources, such as satellite imagery and sensors, to provide actionable insights. This trend helps farmers optimize crop management, predict yields, and detect issues like pest infestations early. The result is more precise and data-driven farming practices that significantly enhance productivity and reduce waste.
• Precision Agriculture Technologies: Precision agriculture technologies are transforming farming by enabling more accurate and efficient use of resources. Technologies such as GPS-guided tractors, variable rate application systems, and remote sensing tools allow farmers to apply inputs like water, fertilizers, and pesticides precisely where needed. This trend reduces the overuse of resources, minimizes environmental impact, and increases crop yields. By leveraging detailed data and advanced analytics, precision agriculture supports sustainable and efficient farming practices.
• IoT and Smart Farming Solutions: The Internet of Things (IoT) plays a crucial role in agriculture analytics by connecting various sensors and devices to collect real-time data from the field. Smart farming solutions utilize this data to monitor soil conditions, weather patterns, and crop health. IoT-enabled devices provide continuous feedback and automation, helping farmers make informed decisions and respond quickly to changing conditions. This trend enhances operational efficiency and supports precision agriculture practices.
• Blockchain for Traceability and Transparency: Blockchain technology is gaining traction in agriculture analytics for its ability to enhance traceability and transparency in the supply chain. By providing a secure and immutable record of transactions, blockchain helps track the origin and journey of agricultural products from farm to table. This trend improves food safety, reduces fraud, and builds consumer trust. It also supports better management of resources and adherence to quality standards.
• Sustainable and Regenerative Farming Analytics: There is a growing emphasis on sustainable and regenerative farming practices supported by advanced analytics. Tools that analyze soil health, biodiversity, and environmental impact are becoming essential for promoting sustainable agriculture. Analytics help farmers implement practices that improve soil fertility, reduce carbon footprints, and enhance ecosystem health. This trend aligns with global efforts to address climate change and promote long-term agricultural sustainability.

These emerging trends significantly reshape the agriculture analytics market by driving innovation and enhancing farming practices. AI, precision agriculture, IoT, blockchain, and sustainability-focused analytics transform how data is used to improve productivity, efficiency, and sustainability in agriculture. As these trends continue to evolve, they promise to address critical challenges and unlock new opportunities for the agricultural sector.

Recent Developments in the Agriculture Analytic Market

The agriculture analytics market reflects the sector's ongoing transformation driven by technological innovation and a focus on optimizing agricultural practices. These advancements make data-driven approaches more accessible and effective, addressing key challenges such as resource management, productivity, and sustainability. By integrating advanced technologies, the agriculture analytics market evolves rapidly, offering new solutions and insights to enhance farming practices across the globe.

• Advanced Sensor Technologies: Recent advancements in sensor technologies have revolutionized data collection in agriculture. Modern sensors, including those for soil moisture, nutrient levels, and weather conditions, provide real-time data that helps farmers make informed decisions. These sensors are increasingly integrated into precision farming systems, allowing for better management of resources and improved crop health monitoring. The enhanced accuracy and timeliness of data support more effective and targeted agricultural practices.
• Big Data and Analytics Platforms: The rise of big data and advanced analytics platforms transforms agriculture by providing deeper insights into farming operations. These platforms analyze large volumes of data from various sources, such as satellites and IoT devices, to offer comprehensive insights into crop performance, soil conditions, and market trends. This development enables farmers to optimize their operations, forecast yields, and make data-driven decisions that enhance productivity and efficiency.
• Autonomous Farming Equipment: Autonomous farming equipment, including drones and self-driving tractors, is becoming increasingly prevalent. These technologies automate tasks such as planting, harvesting, and monitoring crops, reducing the need for manual labor and increasing operational efficiency. Autonomous equipment leverages data from sensors and AI to perform tasks with high precision, leading to more consistent and reliable farming practices.
• Cloud-Based Data Management: Cloud-based data management solutions facilitate the storage, processing, and analysis of agricultural data. These platforms offer scalable and accessible solutions for managing vast amounts of data collected from various sources. Cloud-based systems enable farmers to access real-time insights and collaborate with other stakeholders more effectively. This development supports more efficient data management and enhances decision-making capabilities.
• Integration of Blockchain for Supply Chain Transparency: Blockchain technology is integrated into agriculture analytics to improve supply chain transparency and traceability. By providing a secure and immutable record of transactions, blockchain enhances the ability to track the origin and journey of agricultural products. This development promotes food safety, reduces fraud, and ensures compliance with quality standards, benefiting both producers and consumers.

Recent developments in agriculture analytics significantly impact the sector by introducing advanced technologies and data-driven solutions. The adoption of sensor technologies, big data platforms, autonomous equipment, cloud-based systems, and blockchain enhances productivity, efficiency, and transparency in agriculture. These developments reshape farming practices and address key challenges, paving the way for a more innovative and sustainable agricultural future.

Strategic Growth Opportunities for Agriculture Analytic Market

The agriculture analytics market witnesses significant growth driven by the increasing adoption of data-driven technologies across various applications. As agricultural practices evolve to meet global food demands and sustainability goals, several strategic growth opportunities emerge. These opportunities span different applications within agriculture analytics, offering the potential for innovation and expansion. By focusing on key applications, businesses, and stakeholders can leverage these opportunities to enhance productivity, efficiency, and sustainability in the agricultural sector.

• Precision Agriculture: Precision agriculture is a major growth opportunity within the agriculture analytics market, driven by advancements in GPS technology, sensors, and data analytics. This application allows for the precise management of crop inputs such as water, fertilizers, and pesticides, tailored to specific field conditions. By optimizing resource use and minimizing waste, precision agriculture enhances crop yields and reduces environmental impact. The growing demand for sustainable farming practices and increased efficiency in resource management fuels the expansion of precision agriculture solutions.
• Crop Management and Yield Prediction: Advanced analytics for crop management and yield prediction is a key growth area. By utilizing data from satellite imagery, weather forecasts, and historical crop performance, these tools provide accurate predictions of crop yields and health. This enables farmers to make informed decisions regarding planting, harvesting, and resource allocation. The ability to anticipate crop performance helps optimize agricultural planning and mitigate risks associated with yield variability, supporting overall farm productivity and profitability.
• Soil Health Monitoring: Soil health monitoring is gaining traction as a crucial application of agriculture analytics. Technologies that analyze soil properties, such as nutrient levels, moisture content, and pH, provide valuable insights for soil management. This application helps farmers maintain optimal soil conditions, improve crop growth, and sustain long-term soil fertility. As awareness of soil health's impact on agricultural sustainability grows, investment in soil monitoring solutions increases, presenting significant growth opportunities in this segment.
• Livestock Monitoring: Livestock monitoring is an emerging application of agriculture analytics that focuses on improving animal health and productivity. Solutions that use wearable sensors, GPS tracking, and data analytics provide real-time insights into animal behavior, health status, and productivity. This application enables farmers to detect issues early, optimize feeding practices, and enhance overall herd management. The growing emphasis on animal welfare and efficient livestock management drives the demand for advanced livestock monitoring technologies.
• Supply Chain Optimization: Supply chain optimization is a strategic growth opportunity within agriculture analytics, driven by the need for greater transparency and efficiency in food distribution. Data analytics platforms that track and analyze the movement of agricultural products from farm to table help streamline operations, reduce waste, and improve traceability. By enhancing supply chain management, these solutions support better decision-making and contribute to a more efficient and sustainable agricultural supply chain.

The strategic growth opportunities in agriculture analytics shape the future of the agricultural sector by enhancing precision, efficiency, and sustainability. Applications in precision agriculture, crop management, soil health monitoring, livestock monitoring, and supply chain optimization drive innovation and growth. As these opportunities continue to develop, they are expected to significantly impact agricultural practices, contributing to increased productivity and a more sustainable approach to farming.

Agriculture Analytic Market Driver and Challenges

The agriculture analytics market is influenced by various drivers and challenges that shape its development. Technological advancements, economic factors, and regulatory considerations play crucial roles in driving market growth and addressing obstacles. Understanding these drivers and challenges is essential for stakeholders to navigate the market effectively and leverage opportunities for growth while mitigating risks.

The factors responsible for driving the agriculture analytics market include:

• Technological Advancements: Rapid technological advancements in sensors, data analytics, and AI drive growth in agriculture analytics. Innovations such as advanced sensors for soil and crop monitoring, machine learning algorithms for predictive analytics, and drones for aerial imaging enhance data collection and analysis. These technologies enable precision farming, improve resource management, and optimize crop yields. As technology continues to evolve, it provides new capabilities and efficiencies that propel the agriculture analytics market forward.
• Increasing Demand for Food Security: The growing global population and increasing demand for food are significant drivers of the agriculture analytics market. To meet these demands, there is a need for enhanced agricultural productivity and efficiency. Agriculture analytics provides tools and insights that help optimize crop yields, manage resources effectively, and ensure food security. As the pressure to produce more food intensifies, the adoption of data-driven solutions becomes essential to achieving sustainable agricultural practices.
• Focus on Sustainability: Sustainability is a major driver of agriculture analytics, as there is a strong emphasis on reducing environmental impact and promoting eco-friendly farming practices. Analytics solutions that support precision agriculture, soil health monitoring, and resource optimization align with sustainability goals by minimizing waste and conserving resources. The increasing awareness of environmental issues and regulatory pressure to adopt sustainable practices drive the demand for analytics solutions that contribute to a greener agricultural sector.
• Government Support and Investment: Government initiatives and investments in agricultural technology and data analytics foster market growth. Many governments implement policies and provide funding to support the adoption of advanced technologies in agriculture. These investments help accelerate the development and deployment of agriculture analytics solutions, making them more accessible to farmers and enhancing the overall growth of the market.
• Growing Adoption of IoT and Big Data: The adoption of Internet of Things (IoT) devices and big data analytics drives innovation in agriculture. IoT sensors provide real-time data on various aspects of farming operations, while big data analytics platforms offer insights for decision-making. The integration of these technologies enables farmers to monitor and manage their operations more effectively, leading to increased efficiency and productivity. The widespread adoption of IoT and big data contributes significantly to the growth of agriculture analytics.

Challenges in the agriculture analytics market are:

• High Implementation Costs: One of the key challenges in the agriculture analytics market is the high cost of implementing advanced technologies. The initial investment required for sensors, data analytics platforms, and other technologies can be prohibitive for small and medium-sized farms. High implementation costs may limit the adoption of agriculture analytics solutions and create barriers to entry for some stakeholders, affecting overall market growth.
• Data Privacy and Security Concerns: Data privacy and security concerns are significant challenges in the agriculture analytics market. As agriculture analytics involves the collection and analysis of sensitive data, there is a risk of data breaches and misuse. Ensuring robust data protection measures and addressing privacy concerns are crucial for maintaining trust and encouraging the adoption of analytics solutions. The need for stringent security protocols presents an ongoing challenge for the market.
• Limited Access to Technology in Developing Regions: Limited access to advanced technology and infrastructure in developing regions poses a challenge for the agriculture analytics market. In regions with inadequate technological infrastructure, the adoption of data-driven solutions may be slow. Bridging the technology gap and providing affordable and accessible solutions are essential to expanding the reach of agriculture analytics and ensuring its benefits are realized globally.

The agriculture analytics market is significantly influenced by technological advancements, food security demands, sustainability goals, government support, and the adoption of IoT and big data. However, challenges such as high implementation costs, data privacy concerns, and limited technology access in developing regions also impact the market. Addressing these drivers and challenges is crucial for the continued growth and evolution of agriculture analytics, ensuring that the sector can meet its goals of increased productivity and sustainability.

List of Agriculture Analytic Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. through these strategies agriculture analytic companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the agriculture analytic companies profiled in this report include-

• Deere & Company
• IBM
• Bayer
• SAP
• Trimble
• Accenture
• ABACO
• DeLaval
• Oracle
• DTN

Agriculture Analytic by Segment

The study includes a forecast for the global agriculture analytic by agriculture type, farm size, technology, end use, and region.

Agriculture Analytic Market by Agriculture Type [Analysis by Value from 2019 to 2031]:

• Precision Farming
• Livestock Farming
• Aquaculture Farming
• Vertical Farming
• Others

Agriculture Analytic Market by Farm Size [Analysis by Value from 2019 to 2031]:

• Large Farms
• Small & Medium-Sized Farms

Agriculture Analytic Market by Technology [Analysis by Value from 2019 to 2031]:

• Remote Sensing & Satellite Imagery
• Geographic Information System
• Robotics & Automation
• Big Data & Cloud Computing
• Visualization & Reporting
• Blockchain Technology
• Others

Agriculture Analytic Market by End Use [Analysis by Value from 2019 to 2031]:

• Farmers
• Agronomists
• Agribusinesses
• Agricultural Researchers
• Government Agencies
• Others

Agriculture Analytic Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Agriculture Analytic Market

The agriculture analytics market is rapidly evolving, driven by technological advancements and a growing need for data-driven decision-making in farming. As global food demands rise and sustainability becomes paramount, countries are investing heavily in agricultural technology to optimize crop yields, improve resource management, and enhance overall productivity. Recent developments in the agriculture analytics market reflect this shift, with notable progress across the United States, China, Germany, India, and Japan. These advancements are shaping how data is used to address agricultural challenges and opportunities, transforming traditional farming practices into more efficient, technology-driven processes.

• United States: In the U.S., agriculture analytics is increasingly centered on precision farming and AI-driven insights. Major developments include the integration of satellite imagery and machine learning to optimize crop management. Companies like Trimble and John Deere lead innovations in precision agriculture, offering advanced tools for real-time data collection and analysis. These technologies help farmers increase yield, reduce waste, and manage resources more efficiently. The U.S. also sees significant investment in developing autonomous farming equipment, which aims to further enhance productivity and operational efficiency.
• China: China focuses on leveraging big data and IoT (Internet of Things) to revolutionize its agricultural sector. The government and private enterprises invest in smart farming solutions that include drone technology for crop monitoring and automated systems for precision irrigation. Companies such as DJI and Alibaba are at the forefront, using data analytics to improve crop forecasting and resource management. This push aligns with China's broader strategy to ensure food security and increase agricultural efficiency amid a growing population.
• Germany: Germany's advancements in agriculture analytics are driven by a strong emphasis on sustainability and precision farming. Innovations include the development of advanced sensor technologies and data analytics platforms that monitor soil health and optimize nutrient use. Companies like BASF and Bayer lead these initiatives, focusing on integrating digital solutions into traditional farming practices. Germany's approach highlights the importance of environmentally friendly farming techniques and the efficient use of resources to support sustainable agricultural practices.
• India: In India, agriculture analytics is rapidly evolving with a focus on improving smallholder farmer productivity. Developments include mobile applications and AI-powered platforms that provide real-time weather forecasts, pest detection, and crop recommendations. Startups such as AgroStar and CropIn are making significant strides in offering affordable, accessible analytics tools tailored to the needs of Indian farmers. These innovations aim to address challenges such as low productivity and resource constraints, helping farmers make informed decisions and improve their yields.
• Japan: Japan embraces advanced robotics and data analytics to address labor shortages and enhance agricultural efficiency. Developments include the use of automated systems for planting, harvesting, and monitoring crops. Companies like Kubota and Yamaha develop robotics solutions that integrate with data analytics to optimize farming operations. Additionally, Japan's focus on precision agriculture and smart farming technologies reflects its commitment to maximizing productivity while addressing challenges such as an aging farming population and limited arable land.

Features of the Global Agriculture Analytic Market

Market Size Estimates: Agriculture analytic market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: Agriculture analytic market size by agriculture type, farm size, technology, end use, and region in terms of value ($B).

Regional Analysis: Agriculture analytic market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different agriculture type, farm size, technology, end use, and regions for the agriculture analytic market.

Strategic Analysis: This includes M&A, new product development, and competitive landscape of the agriculture analytic market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the agriculture analytic market by agriculture type (precision farming, livestock farming, aquaculture farming, vertical farming, and others), farm size (large farms and small & medium-sized farms), technology (remote sensing & satellite imagery, geographic information system, robotics & automation, big data & cloud computing, visualization & reporting, blockchain technology, and others), end use (farmers, agronomists, agribusinesses, agricultural researchers, government agencies, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Agriculture Analytic Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Agriculture Analytic Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Agriculture Analytic Market by Agriculture Type
  • 3.3.1: Precision Farming
  • 3.3.2: Livestock Farming
  • 3.3.3: Aquaculture Farming
  • 3.3.4: Vertical Farming
  • 3.3.5: Others
• 3.4: Global Agriculture Analytic Market by Farm Size
  • 3.4.1: Large Farms
  • 3.4.2: Small & Medium-Sized Farms
• 3.5: Global Agriculture Analytic Market by Technology
  • 3.5.1: Remote Sensing & Satellite Imagery
  • 3.5.2: Geographic Information System
  • 3.5.3: Robotics & Automation
  • 3.5.4: Big Data & Cloud Computing
  • 3.5.5: Visualization & Reporting
  • 3.5.6: Blockchain Technology
  • 3.5.7: Others
• 3.6: Global Agriculture Analytic Market by End Use
  • 3.6.1: Farmers
  • 3.6.2: Agronomists
  • 3.6.3: Agribusinesses
  • 3.6.4: Agricultural Researchers
  • 3.6.5: Government Agencies
  • 3.6.6: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Agriculture Analytic Market by Region
• 4.2: North American Agriculture Analytic Market
  • 4.2.1: North American Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.2.2: North American Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.3: European Agriculture Analytic Market
  • 4.3.1: European Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.3.2: European Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.4: APAC Agriculture Analytic Market
  • 4.4.1: APAC Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.4.2: APAC Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others
• 4.5: ROW Agriculture Analytic Market
  • 4.5.1: ROW Agriculture Analytic Market by Agriculture Type: Precision Farming, Livestock Farming, Aquaculture Farming, Vertical Farming, and Others
  • 4.5.2: ROW Agriculture Analytic Market by End Use: Farmers, Agronomists, Agribusinesses, Agricultural Researchers, Government Agencies, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Agriculture Analytic Market by Agriculture Type
  • 6.1.2: Growth Opportunities for the Global Agriculture Analytic Market by Farm Size
  • 6.1.3: Growth Opportunities for the Global Agriculture Analytic Market by Technology
  • 6.1.4: Growth Opportunities for the Global Agriculture Analytic Market by End Use
  • 6.1.5: Growth Opportunities for the Global Agriculture Analytic Market by Region
• 6.2: Emerging Trends in the Global Agriculture Analytic Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Agriculture Analytic Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Agriculture Analytic Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: Deere & Company
• 7.2: IBM
• 7.3: Bayer
• 7.4: SAP
• 7.5: Trimble
• 7.6: Accenture
• 7.7: ABACO
• 7.8: DeLaval
• 7.9: Oracle
• 7.10: DTN