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表紙：農業アナリティクスの世界市場-2023年～2030年
市場調査レポート
商品コード
1319250

農業アナリティクスの世界市場-2023年～2030年

Global Agriculture Analytics Market - 2023-2030
出版日: | 発行: DataM Intelligence | ページ情報: 英文 190 Pages | 納期: 約2営業日

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価格
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農業アナリティクスの世界市場-2023年～2030年
出版日: 2023年07月31日
発行: DataM Intelligence
ページ情報: 英文 190 Pages
納期: 約2営業日
ご注意事項 :
本レポートは最新情報反映のため適宜更新し、内容構成変更を行う場合があります。ご検討の際はお問い合わせください。
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  • 概要
  • 目次
概要

市場概要

農業アナリティクスの世界市場は、2022年に12億米ドルに達し、2023年から2030年の予測期間中にCAGR 11.5％で成長し、2030年には28億米ドルに達すると予測されています。

農業アナリティクスとは、先進技術、データ分析、予測モデリング技術を利用して、農業分野における洞察を深め、情報に基づいた意思決定を行うことを指します。精密農業は、技術や情報を利用して農作業を現場ごとに最適化することを意味します。センサー、ドローン、GPSシステムとともにツールを活用し、土壌状態、作物の生育、環境要因に関する記録を蓄積します。

データ主導型農業とは、センサー、衛星画像、気象観測所など様々なリソースを通じて収集された農業記録を、農業経営における意思決定に役立てることを指します。

人工知能（AI）と機械学習（ML）技術は、農業アナリティクスにおいて重要な役割を果たしています。AIとMLのアルゴリズムは、大量の農業データを分析し、パターンを特定し、予測モデルを生成するために使用されます。これらのモデルは、作物の収量、障害の発生、天候パターンの予測、意思決定と業務効率の改善のための資源配分の最適化を支援します。

市場力学

世界の食糧生産需要の増加が農業アナリティクス市場を牽引

世界人口はかなりの割合で増加し続けています。国連食糧農業機関（FAO）によると、穀物収量の伸びは年率0.7%（発展途上国では0.8%）に減速し、平均穀物収量は2050年までに3.2トン/ヘクタールから約4.3トン/ヘクタールに達します。同じ情報源によれば、世界人口は2050年までに97億人に達すると予測されています。この人口急増は、増加する需要を満たすために余分な食料を供給しなければならないというプレッシャーを農業地帯に与えます。

急速な都市化と生活の変化により、従来の自給的農業から商業的農業への移行が進んでいます。このシフトは、農業慣行の改善と、食糧生産における生産性とパフォーマンスを向上させるための農業アナリティクスとともに、先進技術の採用を必要とします。

モノのインターネット（IoT）とセンサー技術の統合が農業アナリティクス市場を促進する見込み

農業におけるIoTとセンサー技術の混合は、土壌水分、温度、湿度、作物の健康状態など、多数のパラメーターのユニークな追跡と制御を可能にします。世界経済フォーラムの報告書によると、農業にIoTを導入することで、水の使用量を10～15％、化学物質の投入量を20～30％削減することができます。これらのテクノロジーは、農業アナリティクスを通じて分析可能なリアルタイムの統計を提供し、農家が資源配分を最適化し、無駄を削減し、一般的な農業パフォーマンスを飾ることを可能にします。

IoTとセンサー技術により、農地、家畜、機械からの継続的なデータ収集が可能になります。このリアルタイム・データは、農業アナリティクス・ツールを使って分析することで、意思決定のための貴重な洞察を得ることができます。例えば、センサーは土壌水分レベルのデータを提供し、農家は灌漑のスケジュールを正確に立てることができます。リアルタイムのデータ分析により、変化する状況への積極的な対応が可能になり、農場管理方法の改善と生産性の向上につながります。

認識と技術的専門知識の不足が農業アナリティクス市場の足かせに

農業アナリティクスには、データ分析、解釈、アナリティクス・ツールの使用に関する一定レベルの技術的専門知識が必要です。しかし、多くの農家や農業専門家は、これらの分野で必要なスキルや知識を持っていない可能性があります。統計分析、モデリング技術、データの可視化などに精通していない場合もあります。このような知識のギャップは、農業アナリティクス・ソリューションの導入と効果的な活用を妨げる可能性があります。

農業従事者は、農業アナリティクスに関するガイダンスを提供する研修プログラムや支援システムにアクセスする際に課題に直面することが多いです。トレーニングリソースやワークショップ、専門家による支援が限られているため、データ分析に関する技術的専門知識の習得や分析ツールの実用化が妨げられる可能性があります。利用しやすい研修や支援体制の欠如は、技術的専門知識の格差を悪化させる。

COVID-19影響分析

COVID-19分析には、COVID前シナリオ、COVIDシナリオ、COVID後シナリオに加え、価格力学（COVID前シナリオと比較したパンデミック中およびパンデミック後の価格変動を含む）、需給スペクトラム（取引制限、封鎖、およびその後の問題による需要と供給のシフト）、政府の取り組み（政府機関による市場、セクター、産業を復興させる取り組み）、メーカーの戦略的取り組み（COVID問題を緩和するためにメーカーが行ったことをここで取り上げる）が含まれます。

目次

第1章 調査手法と調査範囲

第2章 市場の定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 市場への影響要因
    • 促進要因
    • 抑制要因
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析

第6章 COVID-19分析

第7章 コンポーネント別

  • ソリューション
  • サービス別

第8章 アプリケーション別

  • 農場分析
  • 家畜分析
  • 水産養殖
  • その他

第9章 デプロイメント別

  • クラウド
  • オンプレミス

第10章 農場規模別

  • 大規模農場
  • 中小規模農場

第11章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • スペイン
    • その他欧州
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋地域
  • 中東・アフリカ

第12章 競合情勢

  • 競合シナリオ
  • 市況/シェア分析
  • M&A分析

第13章 企業プロファイル

  • Trimble Inc.
    • 会社概要
    • 製品ポートフォリオと説明
    • 財務概要
    • 主な発展
  • Bayer AG
  • IBM Corporation
  • Deere & Company
  • Ageagle Aerial Systems Inc
  • Vistex, Inc.
  • Agrivi
  • SAS Institute Inc.
  • Conservis Corporation
  • Iteris Inc

第14章 付録

目次
Product Code: AG6574

Market Overview

Global Agriculture Analytics Market reached US$ 1.2 billion in 2022 and is expected to reach US$ 2.8 billion by 2030 growing with a CAGR of 11.5% during the forecast period 2023-2030.

Agriculture analytics refers to the use of advanced technologies, data analysis, and predictive modeling techniques to gain insights and make informed decisions in the field of agriculture. Precision agriculture entails the use of technology and information to optimize farming practices on a site-specific basis. It utilizes tools along with sensors, drones, and GPS systems to accumulate records about soil conditions, crop growth, and environmental factors.

Data-driven farming refers to the practice of using agricultural records, collected through various resources inclusive of sensors, satellite imagery, and weather stations, to inform decision-making in farming operations.

Artificial intelligence (AI) and machine studying (ML) techniques are playing a important role in agriculture analytics. AI and ML algorithms are used to analyze large volumes of agricultural data, identify patterns, and generate predictive models. These models assist in predicting crop yields, disorder outbreaks, weather patterns, and optimizing resource allocation for improved decision-making and operational efficiency.

Market Dynamics

Increasing Demand for Food Production Globally is Driving the Agriculture Analytics Market

The global population continues to increase at a substantial rate. According to the Food and Agriculture Organization (FAO), cereal yield growth would slowdown to 0.7 percent per annum (0.8 percent in developing countries), and average cereal yield would by 2050 reach around 4.3 ton/ha, up from 3.2 ton/ha. According to the same source, the world population is projected to reach 9.7 billion by 2050. This population boom puts pressure at the agriculture zone to supply extra food to fulfill the rising demand.

Rapid urbanization and converting life have brought about a shift from conventional subsistence farming to commercial agriculture. This shift necessitates improved agricultural practices and the adoption of advanced technology, together with agriculture analytics, to boom productivity and performance in food production.

Integration of Internet of Things (IoT) and Sensor Technologies is Expected to Foster the Agriculture Analytics Market

The mixing of IoT and sensor technology in agriculture enables unique tracking and control of numerous parameters such as soil moisture, temperature, humidity, and crop health. According to a report by the World Economic Forum, the adoption of IoT in agriculture can lead to a 10-15% reduction in water usage and a 20-30% reduction in chemical inputs. Those technologies provide real-time statistics that may be analyzed through agriculture analytics, allowing farmers to optimize resource allocation, reduce waste, and decorate common farming performance.

IoT and sensor technology enable continuous data collection from agricultural fields, livestock, and machinery. This real-time data can be analyzed using agriculture analytics tools to provide valuable insights for decision-making. For instance, sensors can provide data on soil moisture levels, allowing farmers to precisely schedule irrigation. Real-time data analysis enables proactive responses to changing conditions, leading to improved farm management practices and increased productivity.

Lack of Awareness and Technical Expertise is Holding Back the Agriculture Analytics Market

Agriculture analytics requires a certain level of technical expertise in data analysis, interpretation, and the use of analytics tools. However, many farmers and agricultural professionals may not possess the necessary skills or knowledge in these areas. They may lack familiarity with statistical analysis, modeling techniques, and data visualization. This knowledge gap can impede the implementation and effective utilization of agriculture analytics solutions.

Farmers often face challenges in accessing training programs or support systems that provide guidance on agriculture analytics. Limited availability of training resources, workshops, or expert assistance can hinder the development of technical expertise in data analysis and the practical application of analytics tools. The lack of accessible training and support mechanisms exacerbates the gap in technical expertise.

COVID-19 Impact Analysis

The COVID-19 Analysis includes Pre-COVID Scenario, COVID Scenario, and Post-COVID Scenario along with Pricing Dynamics (Including pricing change during and post-pandemic comparing it with pre-COVID scenarios), Demand-Supply Spectrum (Shift in demand and supply owing to trading restrictions, lockdown, and subsequent issues), Government Initiatives (Initiatives to revive market, sector or Industry by Government Bodies) and Manufacturers Strategic Initiatives (What manufacturers did to mitigate the COVID issues will be covered here).

Segment Analysis

The global agriculture analytics market is segmented based on source, packaging, distribution channel, and region.

By Deployment, the Cloud Segment is Estimated to have Significant Growth During the Forecast Period

Cloud-based solutions offer scalability and flexibility, allowing users to scale their storage and computing resources based on their needs. This scalability is particularly valuable in the agriculture industry, where data volumes can vary significantly throughout the agricultural cycle. According to a journal published by Frontiers, Automation and the use of artificial intelligence (AI), internet of things (IoT), drones, robots, and Big Data serve as a basis for a global "Digital Twin," which will contribute to the development of site-specific conservation and management practices that will increase incomes and global sustainability of agricultural systems.

Cloud-based agriculture analytics platforms can accommodate the storage and processing requirements of large and diverse agricultural datasets. The cloud enables easy access to data from anywhere, anytime, as long as there is an internet connection. This accessibility promotes collaboration and data sharing among stakeholders in the agriculture ecosystem, including farmers, researchers, consultants, and agribusinesses. Cloud-based platforms facilitate real-time data access, analytics, and collaborative decision-making, contributing to the overall adoption and dominance of the cloud segment.

Geographical Analysis

Asia Pacific is the Fastest Growing Market in the Agriculture Analytics Market

Asia Pacific is domestic to a large agricultural area and a vast population engaged in farming. The increasing demand for meals, coupled with the need to beautify agricultural productiveness, has led to the adoption of superior technology and analytics solutions. According to Asia Development Bank, with 76% of Asia's poor living in rural areas, raising agricultural productivity and income is key to fighting poverty. By leveraging agriculture analytics, farmers in the region can optimize resource utilization, implement precision farming practices, and improve overall productivity.

Precision agriculture techniques, which heavily rely upon data-driven insights and analytics, have received traction inside the Asia Pacific region. Farmers are increasingly more adopting technology inclusive of sensors, drones, and satellite imagery to monitor crops, analyze soil conditions, and optimize resource management. Agriculture analytics performs a important role in analyzing the collected data and supplying actionable insights for precision agriculture, contributing to the market growth inside the region.

Competitive Landscape

The major global players in the market include: Trimble Inc., Bayer AG, IBM Corporation, Deere & Company, Ageagle Aerial Systems Inc, Vistex, Inc., Agrivi, SAS Institute Inc., Conservis Corporation, and Iteris Inc.

Why Purchase the Report?

  • To visualize the global agriculture analytics market segmentation based on component, application, deployment, farm size, and region, as well as understand key commercial assets and players.
  • Identify commercial opportunities in the market by analyzing trends and co-development.
  • Excel data sheet with numerous data points of agriculture analytics market-level with all segments.
  • The PDF report consists of cogently put-together market analysis after exhaustive qualitative interviews and in-depth market study.
  • Product mapping is available as Excel consists of key products of all the major market players.

The global agriculture analytics market report would provide approximately 69 tables, 65 figures and 190 Pages.

Target Audience 2023

  • Manufacturers/ Buyers
  • Industry Investors/Investment Bankers
  • Research Professionals
  • Emerging Companies

Table of Contents

1. Methodology and Scope

  • 1.1. Research Methodology
  • 1.2. Research Objective and Scope of the Report

2. Market Definition and Overview

3. Executive Summary

  • 3.1. Market Snippet, by Component
  • 3.2. Market Snippet, by Application
  • 3.3. Market Snippet, by Deployment
  • 3.4. Market Snippet, by Farm Size
  • 3.5. Market Snippet, by Region

4. Market Dynamics

  • 4.1. Market Impacting Factors
    • 4.1.1. Drivers
    • 4.1.2. Restraints
    • 4.1.3. Opportunity
    • 4.1.4. Impact Analysis

5. Industry Analysis

  • 5.1. Porter's Five Force Analysis
  • 5.2. Supply Chain Analysis
  • 5.3. Pricing Analysis
  • 5.4. Regulatory Analysis

6. COVID-19 Analysis

  • 6.1. Analysis of COVID-19
    • 6.1.1. Scenario Before COVID-19
    • 6.1.2. Scenario During COVID-19
    • 6.1.3. Scenario Post COVID-19
  • 6.2. Pricing Dynamics Amid COVID-19
  • 6.3. Demand-Supply Spectrum
  • 6.4. Government Initiatives Related to the Market During Pandemic
  • 6.5. Manufacturers Strategic Initiatives
  • 6.6. Conclusion

7. By Component

  • 7.1. Introduction
    • 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 7.1.2. Market Attractiveness Index, By Component
  • 7.2. Solution*
    • 7.2.1. Introduction
    • 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 7.3. Services

8. By Application

  • 8.1. Introduction
    • 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 8.1.2. Market Attractiveness Index, By Application
  • 8.2. Farm Analytics*
    • 8.2.1. Introduction
    • 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 8.3. Livestock Analytics
  • 8.4. Aquaculture
  • 8.5. Others

9. By Deployment

  • 9.1. Introduction
    • 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 9.1.2. Market Attractiveness Index, By Deployment
  • 9.2. Cloud*
    • 9.2.1. Introduction
    • 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 9.3. On-Premises

10. By Farm Size

  • 10.1. Introduction
    • 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 10.1.2. Market Attractiveness Index, By Farm Size
  • 10.2. Large Farms*
    • 10.2.1. Introduction
    • 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  • 10.3. Small & Medium Farms

11. By Region

  • 11.1. Introduction
    • 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
    • 11.1.2. Market Attractiveness Index, By Region
  • 11.2. North America*
    • 11.2.1. Introduction
    • 11.2.2. Key Region-Specific Dynamics
    • 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.2.7.1. The U.S.
      • 11.2.7.2. Canada
      • 11.2.7.3. Mexico
  • 11.3. Europe
    • 11.3.1. Introduction
    • 11.3.2. Key Region-Specific Dynamics
    • 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.3.7.1. Germany
      • 11.3.7.2. The U.K.
      • 11.3.7.3. France
      • 11.3.7.4. Italy
      • 11.3.7.5. Spain
      • 11.3.7.6. Rest of Europe
  • 11.4. South America
    • 11.4.1. Introduction
    • 11.4.2. Key Region-Specific Dynamics
    • 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.4.7.1. Brazil
      • 11.4.7.2. Argentina
      • 11.4.7.3. Rest of South America
  • 11.5. Asia-Pacific
    • 11.5.1. Introduction
    • 11.5.2. Key Region-Specific Dynamics
    • 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size
    • 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
      • 11.5.7.1. China
      • 11.5.7.2. India
      • 11.5.7.3. Japan
      • 11.5.7.4. Australia
      • 11.5.7.5. Rest of Asia-Pacific
  • 11.6. Middle East and Africa
    • 11.6.1. Introduction
    • 11.6.2. Key Region-Specific Dynamics
    • 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
    • 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
    • 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
    • 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Farm Size

12. Competitive Landscape

  • 12.1. Competitive Scenario
  • 12.2. Market Positioning/Share Analysis
  • 12.3. Mergers and Acquisitions Analysis

13. Company Profiles

  • 13.1. Trimble Inc.*
    • 13.1.1. Company Overview
    • 13.1.2. Product Portfolio and Description
    • 13.1.3. Financial Overview
    • 13.1.4. Key Developments
  • 13.2. Bayer AG
  • 13.3. IBM Corporation
  • 13.4. Deere & Company
  • 13.5. Ageagle Aerial Systems Inc
  • 13.6. Vistex, Inc.
  • 13.7. Agrivi
  • 13.8. SAS Institute Inc.
  • 13.9. Conservis Corporation
  • 13.10. Iteris Inc

LIST NOT EXHAUSTIVE

14. Appendix

  • 14.1. About Us and Services
  • 14.2. Contact Us