農業分析市場 - 按產品（軟體、服務）、按農場規模（中小型農場、大型農場）、按應用（畜牧業、水產養殖、精準農業、傳統農業）、按技術和預測，2024 - 2032

由於人工智慧 (AI)、機器學習和巨量資料分析等先進技術在農業領域的採用，農業分析市場預計在 2024 年至 2032 年期間將呈現 10% 的複合年成長率。這些技術為農民提供了有關作物健康、土壤條件和天氣的見解，從而實現數據驅動的決策。在這些技術的推動下，精準農業可以最佳化資源利用、提高產量並減少對環境的影響。無人機和物聯網感測器等智慧農業工具進一步增強資料收集和分析，改善農業實踐和生產力。

全球對糧食安全和永續農業的需求正在推動市場成長。根據聯合國的估計，到 2050 年，世界人口預計將達到 97 億。 農業分析有助於管理資源和最佳化作物，提高生產力，同時最大限度地減少浪費和環境危害。政府對農業技術的措施和投資也促進了分析技術的採用。例如，2024 年4 月，美國農業部宣佈在2024 會計年度歷史性地投資15 億美元，透過區域保護夥伴計畫(RCPP) 支持合作夥伴驅動的保育和氣候解決方案，與拜登總統的投資美國計劃保持一致。這些努力旨在加強糧食安全並應對氣候變遷和資源短缺的挑戰。

整個農業分析產業根據產品、農場規模、應用、技術和地區進行分類。

由於精準農業應用能夠徹底改變傳統農業實踐，因此到 2032 年，精準農業應用將實現強勁成長。透過利用衛星影像、GPS 和物聯網感測器等各種來源的資料，精準農業使農民能夠根據具體的田間條件實施有針對性的干涉措施。這種方法提高了資源利用效率，最佳化了作物管理，並最大限度地提高了產量，同時最大限度地減少了對環境的影響。精準農業解決方案有助於即時監測土壤健康、天氣模式和作物發育，從而可以精確調整灌溉、施肥和病蟲害防治。將先進分析技術融入精準農業不僅可以提高生產力，還可以透過減少浪費和改善整體農場管理來支持永續農業實踐。

到 2032 年，供應鏈分析領域將佔據顯著的市場佔有率，因為它可以更有效地解決管理農業供應鏈的複雜性。透過利用先進的資料分析和預測模型，利害關係人能夠深入了解供應鏈的每個階段，從生產和加工到分銷和零售。這種增強的可視性有助於最佳化物流、降低成本並提高需求預測的準確性。透過整合天氣狀況、市場趨勢和運輸物流等各種來源的資料，供應鏈分析技術可以增強決策、降低風險並確保生鮮農產品的及時交付。

在該地區重要的農業基礎和不斷增加的技術投資的推動下，亞太地區農業分析產業將在 2032 年實現快速擴張。人工智慧、機器學習和物聯網等先進技術在農業中的採用正在幫助農民最佳化作物產量、更有效地管理資源並改善供應鏈管理。此外，政府對數位農業創新的措施和支持正在促進市場成長。隨著亞太國家尋求農業實踐現代化，以滿足人口成長和氣候條件變化的需求，市場參與者將發現利潤豐厚的成長機會。

目錄

第 1 章：方法與範圍

第 2 章：執行摘要

第 3 章：產業洞察

• 產業生態系統分析
• 供應商格局
  • 軟體供應商
  • 服務商
  • 技術提供者
  • 終端用戶
• 利潤率分析
• 技術與創新格局
• 專利分析
• 重要新聞和舉措
• 監管環境
• 衝擊力
  • 成長動力
    • 網路安全事件增多
    • 嚴格的監管合規性，嚴格的資料保護
    • 越來越多採用數位平台
    • 品牌聲譽問題日益嚴重
    • 人工智慧和機器學習的日益普及
  • 產業陷阱與挑戰
    • 監理合規的複雜性
• 成長潛力分析
• 波特的分析
• PESTEL分析

第 4 章：競爭格局

• 介紹
• 公司市佔率分析
• 競爭定位矩陣
• 戰略展望矩陣

第 5 章：市場估計與預測：依產品分類，2021 - 2032 年

• 主要趨勢
• 軟體
• 服務
  • 專業的
  • 託管

第 6 章：市場估計與預測：依農場規模，2021 - 2032

• 主要趨勢
• 大型農場
• 中小型農場

第 7 章：市場估計與預測：依應用分類，2021 - 2032

• 主要趨勢
• 畜牧業
• 水產養殖
• 精準農業
• 傳統農業
• 其他

第 8 章：市場估計與預測：依技術分類，2021 - 2032

• 主要趨勢
• 牲畜分析
  • 產量圖
  • 現場監控
  • 天氣追蹤
  • 其他
• 供應鏈分析
• 農場分析
• 其他

第 9 章：市場估計與預測：按地區，2021 - 2032

• 主要趨勢
• 北美洲
  • 美國
  • 加拿大
• 歐洲
  • 英國
  • 德國
  • 法國
  • 義大利
  • 西班牙
  • 俄羅斯
  • 歐洲其他地區
• 亞太地區
  • 中國
  • 印度
  • 日本
  • 韓國
  • 澳新銀行
  • 東南亞
  • 亞太地區其他地區
• 拉丁美洲
  • 巴西
  • 墨西哥
  • 阿根廷
  • 拉丁美洲其他地區
• MEA
  • 阿拉伯聯合大公國
  • 南非
  • 沙烏地阿拉伯
  • MEA 的其餘部分

第 10 章：公司簡介

• Accenture
• AGCO Corporation
• Bayer
• Corteva Agriscience
• Deere and Co.
• DeLaval
• Geosis, Inc.
• IBM
• Oracle
• PrecisionHawk
• SAP SE
• Taranis
• Trimble
• Wipro
• Xylem, Inc.

The agriculture analytics market is projected to exhibit 10% CAGR over 2024-2032, due to the adoption of advanced technologies like artificial intelligence (AI), machine learning, and big data analytics in agriculture sector. These technologies provide farmers with insights into crop health, soil conditions, and weather, enabling data-driven decisions. Precision agriculture, driven by these technologies, optimizes resource use, increasing yields and reducing environmental impact. Smart farming tools, such as drones and IoT sensors, further enhance data collection and analysis, improving farming practices and productivity.

The global demand for food security and sustainable agriculture are augmenting the market growth. As per the UN estimates, the world population is projected to reach 9.7 billion by 2050. Agriculture analytics help manage resources and optimize crops, boosting productivity while minimizing waste and environmental harm. Government initiatives and investments in agricultural technology are also promoting analytics adoption. For instance, in April 2024, The USDA announced a historic $1.5 billion investment in fiscal year 2024 to support partner-driven conservation and climate solutions through the Regional Conservation Partnership Program (RCPP), aligning with President Biden's Investing in America initiative. These efforts aim to enhance food security and address climate change and resource scarcity challenges.

The overall agriculture analytics industry is classified based on offering, farm size, application, technology, and region.

The precision farming application is set to experience robust growth through 2032, due to its ability to revolutionize traditional farming practices. By leveraging data from various sources such as satellite imagery, GPS, and IoT sensors, precision farming enables farmers to implement targeted interventions tailored to specific field conditions. This approach enhances the efficiency of resource use, optimizes crop management, and maximizes yields while minimizing environmental impact. Precision farming solutions facilitate real-time monitoring of soil health, weather patterns, and crop development, allowing for precise adjustments in irrigation, fertilization, and pest control. The integration of advanced analytics into precision farming not only boosts productivity but also supports sustainable agricultural practices by reducing waste and improving overall farm management.

The supply chain analytics segment will hold a notable market share by 2032, as it addresses the complexities of managing agricultural supply chains more effectively. By utilizing advanced data analytics and predictive modeling, it enables stakeholders to gain deep insights into every stage of the supply chain, from production and processing to distribution and retail. This enhanced visibility helps optimize logistics, reduce costs, and improve the accuracy of demand forecasting. By integrating data from various sources, such as weather conditions, market trends, and transportation logistics, supply chain analytics technology enhances decision-making, mitigates risks, and ensures the timely delivery of fresh produce.

Asia Pacific agriculture analytics industry will record rapid expansion through 2032, driven by the region's significant agricultural base and increasing investments in technology. The adoption of advanced technologies such as AI, machine learning, and IoT in agriculture is helping farmers optimize crop yields, manage resources more efficiently, and improve supply chain management. Additionally, government initiatives and support for digital agriculture innovations are bolstering market growth. As Asia-Pacific countries seek to modernize their agricultural practices to meet the demands of a growing population and changing climate conditions, the market players will find lucrative opportunities for growth.

Table of Contents

Chapter 1 Methodology & Scope

• 1.1 Market scope & definition
• 1.2 Research design
  • 1.2.1 Research approach
  • 1.2.2 Data collection methods
• 1.3 Base estimates & calculations
  • 1.3.1 Base year calculation
  • 1.3.2 Key trends for market estimation
• 1.4 Forecast model
• 1.5 Primary research and validation
  • 1.5.1 Primary sources
  • 1.5.2 Data mining sources

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2021 - 2032

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
• 3.2 Supplier landscape
  • 3.2.1 Software providers
  • 3.2.2 Service provider
  • 3.2.3 Technology providers
  • 3.2.4 End-user
• 3.3 Profit margin analysis
• 3.4 Technology & innovation landscape
• 3.5 Patent analysis
• 3.6 Key news & initiatives
• 3.7 Regulatory landscape
• 3.8 Impact forces
  • 3.8.1 Growth drivers
    • 3.8.1.1 Increasing cyber security incidents
    • 3.8.1.2 Stringent regulatory compliance for strict data protection
    • 3.8.1.3 Growing adoption of digital platforms
    • 3.8.1.4 Increasing brand reputation concerns
    • 3.8.1.5 Growing adoption of artificial intelligence and machine learning
  • 3.8.2 Industry pitfalls & challenges
    • 3.8.2.1 Complexity of regulatory compliance
• 3.9 Growth potential analysis
• 3.10 Porter's analysis
• 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

• 4.1 Introduction
• 4.2 Company market share analysis
• 4.3 Competitive positioning matrix
• 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Offering, 2021 - 2032 ($Bn)

• 5.1 Key trends
• 5.2 Software
• 5.3 Services
  • 5.3.1 Professional
  • 5.3.2 Managed

Chapter 6 Market Estimates & Forecast, By farm size, 2021 - 2032 ($Bn)

• 6.1 Key trends
• 6.2 Large farms
• 6.3 Small and medium farms

Chapter 7 Market Estimates & Forecast, By Application, 2021 - 2032 ($Bn)

• 7.1 Key trends
• 7.2 Livestock farming
• 7.3 Aquaculture farming
• 7.4 Precision farming
• 7.5 Conventional farming
• 7.6 Others

Chapter 8 Market Estimates & Forecast, By Technology, 2021 - 2032 ($Bn)

• 8.1 Key trends
• 8.2 Livestock analytics
  • 8.2.1 Yield mapping
  • 8.2.2 Field monitoring
  • 8.2.3 Weather tracking
  • 8.2.4 Others
• 8.3 Supply chain analytics
• 8.4 Farm analytics
• 8.5 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2032 ($Bn)

• 9.1 Key trends
• 9.2 North America
  • 9.2.1 U.S.
  • 9.2.2 Canada
• 9.3 Europe
  • 9.3.1 UK
  • 9.3.2 Germany
  • 9.3.3 France
  • 9.3.4 Italy
  • 9.3.5 Spain
  • 9.3.6 Russia
  • 9.3.7 Rest of Europe
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 India
  • 9.4.3 Japan
  • 9.4.4 South Korea
  • 9.4.5 ANZ
  • 9.4.6 Southeast Asia
  • 9.4.7 Rest of Asia Pacific
• 9.5 Latin America
  • 9.5.1 Brazil
  • 9.5.2 Mexico
  • 9.5.3 Argentina
  • 9.5.4 Rest of Latin America
• 9.6 MEA
  • 9.6.1 UAE
  • 9.6.2 South Africa
  • 9.6.3 Saudi Arabia
  • 9.6.4 Rest of MEA

Chapter 10 Company Profiles

• 10.1 Accenture
• 10.2 AGCO Corporation
• 10.3 Bayer
• 10.4 Corteva Agriscience
• 10.5 Deere and Co.
• 10.6 DeLaval
• 10.7 Geosis, Inc.
• 10.8 IBM
• 10.9 Oracle
• 10.10 PrecisionHawk
• 10.11 SAP SE
• 10.12 Taranis
• 10.13 Trimble
• 10.14 Wipro
• 10.15 Xylem, Inc.