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農業技術轉型評估：發展、成就與未來展望（2010-2020年和2020-2030年）
市場調查報告書
商品編碼
1617395

農業技術轉型評估：發展、成就與未來展望（2010-2020年和2020-2030年）

Agriculture Technology Transition Assessment: Evaluating Developments, Achievements, and Future Prospects, 2010-2020 and 2020-2030
出版日期: | 出版商: BIS Research | 英文 131 Pages | 商品交期: 1-5個工作天內

價格

農業技術的引進徹底改變了作物生產、牲畜管理、水產養殖和林業。

在農作物生產中，精密農業工具可以提高產量並減少投入浪費。牲畜管理可以受益於自動監測系統，以改善牲畜健康和生產力。水產養殖利用先進的感測器和資料分析來實現高效的飼養和水質管理。透過遙感探測和地理資訊系統技術最佳化林業，以幫助實現永續森林管理。這些進步正在提高所有部門的生產力、資源效率和環境永續性。採用技術對於滿足世界糧食需求和應對氣候變遷和資源稀缺等挑戰至關重要。

2010 年至 2020 年期間，作物管理軟體發生了革命性的發展。 FMIS、GIS、遙感探測和物聯網等技術已成為現代農業不可或缺的一部分，實現了精密農業和永續實踐。隨著這些技術的不斷發展，我們希望進一步提高農業生產力，增強應對世界日益嚴峻的挑戰的能力。

從2010年到2020年，精密農業取得了重大技術進步，徹底改變了作物生產。從早期採用 GPS 進行田間測繪到整合先進的農場管理系統，這些技術使農民能夠最佳化投入使用、提高生產力並提高永續性。

從2016年到2020年，無線網路和物聯網的整合實現了持續監控和資料收集。這些資料經過即時分析，可以根據感測器讀數立即採取行動，以改善作物健康和產量。

農業技術轉型評估審查了2010年至2020年和2020年至2030年的農業技術發展、成就和未來前景。該研究涵蓋作物生產、畜牧業、水產養殖業和林業，並專注於先進農業技術的生產、開發、分配和維護。它評估了精密農業、牲畜管理系統、水產養殖監測和林業管理工具的創新如何改變了農業實踐。

目錄

執行摘要

第1章 歷史回顧：2010-2020

  • 農作物生產的主要技術進展
    • 精密農業
    • 基因改造生物 (GMO) 和雜交種子
    • 自動化機械設備
    • 作物管理軟體
  • 畜牧業技術進步
    • 動物健康監測系統
    • 基因工程與育種創新
    • 牲畜管理軟體
  • 水產養殖技術的進步
    • 水產養殖監控系統
    • 育種和遺傳學
    • 水質管理
  • 林業科技創新
    • 遙感探測和地理資訊系統
    • 永續森林管理
    • 木材切割設備

第2章 現況：2020-2030

  • 農作物生產技術發展
    • 精密農業的進展
    • 作物遺傳學的創新
    • 整合物聯網和人工智慧
  • 畜牧生產技術
    • 智慧動物健康監測
    • 精準畜牧業
    • 家畜遺傳學進展
  • 水產養殖進步
    • 水產養殖中的物聯網與人工智慧
    • 永續飼料和營養
    • 養殖技術創新
  • 林業科技趨勢
    • 精準林業
    • 先進的收穫技術

第3章 比較分析：2010-2020年及2020-2030年

  • 農作物生產技術進步
    • 精密農業
    • 基因改造和雜交種子
    • 自動化機械設備
    • 作物管理軟體
    • 整合物聯網和人工智慧
  • 畜牧生產創新
    • 動物健康監測系統
    • 基因工程與育種創新
    • 牲畜管理軟體
    • 智慧動物健康監測
    • 精準畜牧業
    • 家畜遺傳學進展
  • 水產養殖的進步
    • 水產養殖監控系統
    • 育種和遺傳學
    • 水質管理
    • 水產養殖中的物聯網與人工智慧
    • 永續飼料和營養
    • 養殖技術創新
  • 林業科技的演變
    • 遙感探測和地理資訊系統
    • 永續森林管理
    • 木材切割設備
    • 精準林業

第 4 章 結果與影響：案例研究

  • 提高作物產量
  • 提高畜牧業生產力
  • 提高水產養殖效率
  • 永續森林管理

第5章挑戰與機遇

  • 技術採用的障礙
    • 農作物生產
    • 家畜
    • 水產養殖
    • 林業
  • 政府措施和影響
  • 投資金籌措趨勢

第6章 建議與結論

第7章調查方法

Product Code: AGA2136SA

Agriculture Technology Transition Assessment

The agriculture technology transition assessment examines agricultural technologies' developments, achievements, and future prospects from 2010 to 2020 and 2020 to 2030. This study encompasses crop production, livestock, aquaculture, and forestry, focusing on the industry's production, development, distribution, and maintenance of advanced agricultural technologies. It evaluates how innovations in precision farming, livestock management systems, aquaculture monitoring, and forestry management tools have transformed agricultural practices. The assessment aims to provide a comprehensive overview of technological advancements, their impact on productivity and sustainability, and projections for future trends and opportunities in the agricultural sector.

Industrial Impact

Technology adoption in agriculture has revolutionized crop production, livestock management, aquaculture, and forestry. In crop production, precision farming tools enhance yields and reduce input wastage. Livestock management benefits from automated monitoring systems, improving animal health and productivity. Aquaculture utilizes advanced sensors and data analytics for efficient feeding and water quality management. Forestry operations are optimized with remote sensing and GIS technologies, aiding in sustainable forest management. These advancements lead to increased productivity, resource efficiency, and environmental sustainability across all sectors. Technology adoption is crucial for meeting global food demand and addressing challenges such as climate change and resource scarcity.

Developments in the Agriculture Technology Transition:

  • The period from 2010 to 2020 witnessed transformative developments in crop management software. Technologies such as FMIS, GIS, remote sensing, and IoT have become integral to modern agriculture, enabling precision farming and sustainable practices. As these technologies continue to evolve, they promise to enhance further agricultural productivity and resilience in the face of growing global challenges.
  • Between 2010 and 2020, precision agriculture witnessed remarkable technological advancements that significantly transformed crop production. From the early adoption of GPS for field mapping to integrating sophisticated farm management systems, these technologies have enabled farmers to optimize input use, enhance productivity, and improve sustainability.
  • In the period between 2016-2020, the integration of wireless networks and IoT enabled continuous monitoring and data collection. This data could be analyzed in real-time, allowing immediate action based on sensor readings, enhancing crop health and yield.

How can this report add value to an organization?

Innovation Strategy: This provides valuable insights into the methods and innovations shaping agriculture's shift toward technology-driven sustainability. By conducting a thorough agriculture technology transition assessment, stakeholders can evaluate the challenges and opportunities that come with adopting smart agriculture solutions. Such an assessment enables policymakers, agribusinesses, and farmers to understand the environmental, economic, and operational impacts of transitioning to technologies that support efficient resource use, reduce emissions, and enhance crop yields.

Growth/Marketing Strategy: The transition to advanced agriculture technology has led to developments in the sector, as key players utilize assessments to guide product innovation, market expansion, and strategic alliances. Companies have increasingly conducted thorough assessments of technology integration to meet sustainability goals, enhance efficiency, and support regulatory compliance. Partnerships, collaborations, and joint ventures with technology providers and research institutions enable firms to implement cutting-edge solutions, from precision farming and AI-driven analytics to automated machinery.

Competitive Strategy: The agriculture technology transition assessment provides an in-depth smart agriculture analysis, highlighting the unique challenges and opportunities in each area. This assessment offers a comprehensive comparative analysis of the technologies in the market. Stakeholders gain a clear perspective on the competitive landscape by evaluating factors such as infrastructure readiness, regulatory frameworks, and technology adoption rates. This enables agribusinesses, policymakers, and investors to identify strategic areas for growth, align with global sustainability goals, and make informed decisions in adapting to a rapidly evolving agricultural technology market.

Research Methodology

Primary Data Sources

The primary sources involve the smart agriculture industry experts and stakeholders such as platform developers and service providers. Respondents such as vice presidents, CEOs, marketing directors, and technology and innovation directors have been interviewed to verify this research study's qualitative and quantitative aspects.

The key data points taken from primary sources include:

  • validation and triangulation of all the numbers and graphs
  • understanding the competitive landscape of different technologies

Secondary Data Sources

This research study involves the usage of extensive secondary research, directories, company websites, and annual reports. It also makes use of databases, such as Hoovers, Bloomberg, Businessweek, and Factiva, to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of the global market. In addition to the aforementioned data sources, the study has been undertaken with the help of other data sources and websites, such as www.fao.org and www.worldbank.org.

Secondary research was done to obtain crucial information about the industry's value chain, revenue models, the market's monetary chain, the total pool of key players, and the current and potential use cases and applications.

The key data points taken from secondary research include:

  • qualitative insights into various aspects of the market, key trends, and emerging areas of innovation
  • quantitative data for mathematical and statistical calculations

Table of Contents

Executive Summary

Scope and Definition

1 Historical Overview: 2010-2020

  • 1.1 Major Technological Developments in Crop Production
    • 1.1.1 Precision Agriculture
      • 1.1.1.1 Global Positioning System (GPS)
      • 1.1.1.2 Variable Rate Technology (VRT)
      • 1.1.1.3 Soil and Crop Sensors
      • 1.1.1.4 Remote Sensing and Imagery
      • 1.1.1.5 Automated Machinery and Robots
      • 1.1.1.6 Integrated Farm Management Systems (IFMS)
    • 1.1.2 Genetically Modified Organisms (GMOs) and Hybrid Seeds
      • 1.1.2.1 Genetically Modified Organisms
        • 1.1.2.1.1 Technological Developments in GMOs, 2010-2020
          • 1.1.2.1.1.1 Herbicide-Resistant Crops:
          • 1.1.2.1.1.2 Insect-Resistant Crops:
          • 1.1.2.1.1.3 Drought-Tolerant Crops:
          • 1.1.2.1.1.4 Nutritionally Enhanced Crops:
          • 1.1.2.1.1.5 Virus-Resistant Crops:
      • 1.1.2.2 Hybrid Seeds
        • 1.1.2.2.1 Technological Developments in Hybrid Seeds, 2010-2020:
          • 1.1.2.2.1.1 High-Yielding Hybrid Varieties:
          • 1.1.2.2.1.2 Disease-Resistant Hybrids:
          • 1.1.2.2.1.3 Stress-Tolerant Hybrids:
          • 1.1.2.2.1.4 Improved Nutritional Quality:
          • 1.1.2.2.1.5 Short-Duration Hybrids:
    • 1.1.3 Automated Machinery and Equipment
      • 1.1.3.1 Technological Developments in Automated Machinery and Equipment, 2010-2020
        • 1.1.3.1.1 Automated Tractors
        • 1.1.3.1.2 Drones
        • 1.1.3.1.3 Harvesting Robots
        • 1.1.3.1.4 Seeding and Planting Robots
        • 1.1.3.1.5 Irrigation Systems
        • 1.1.3.1.6 Weeding Robots
        • 1.1.3.1.7 Spraying Drones
        • 1.1.3.1.8 Soil Health Monitoring Robots
        • 1.1.3.1.9 Automated Guided Vehicles (AGVs)
    • 1.1.4 Crop Management Software
      • 1.1.4.1 Technologies Within Crop Management Software, 2010-2020:
        • 1.1.4.1.1 Farm Management Information Systems (FMIS)
        • 1.1.4.1.2 Geographic Information Systems (GIS)
        • 1.1.4.1.3 Remote Sensing
        • 1.1.4.1.4 Variable Rate Technology (VRT)
        • 1.1.4.1.5 Decision Support Systems (DSS)
        • 1.1.4.1.6 Internet of Things (IoT)
        • 1.1.4.1.7 Cloud Computing
        • 1.1.4.1.8 Big Data Analytics
        • 1.1.4.1.9 Blockchain Technology
  • 1.2 Technological Advancements in Livestock Production
    • 1.2.1 Animal Health Monitoring Systems
      • 1.2.1.1 Technological Advancements in Livestock Production:
        • 1.2.1.1.1 Wearable Sensors
        • 1.2.1.1.2 Automated Milking Systems (AMS)
        • 1.2.1.1.3 Radio Frequency Identification (RFID)
        • 1.2.1.1.4 Drones and Aerial Imaging
        • 1.2.1.1.5 Internet of Things (IoT)
        • 1.2.1.1.6 Machine Learning and Predictive Analytics
    • 1.2.2 Genetic Engineering and Breeding Innovations
      • 1.2.2.1 CRISPR-Cas9 Gene Editing
      • 1.2.2.2 Marker-Assisted Selection (MAS)
      • 1.2.2.3 Genomic Selection
      • 1.2.2.4 Cloning and Somatic Cell Nuclear Transfer (SCNT)
      • 1.2.2.5 Embryo Transfer and In Vitro Fertilization (IVF)
      • 1.2.2.6 Artificial Insemination
    • 1.2.3 Livestock Management Software
      • 1.2.3.1 Automated Data Collection and Analysis
      • 1.2.3.2 Precision Feeding Systems
      • 1.2.3.3 Health Monitoring and Disease Detection
      • 1.2.3.4 Genetic and Breeding Management
      • 1.2.3.5 Farm Management Systems Integration
      • 1.2.3.6 Blockchain
  • 1.3 Progress in Aquaculture Technology
    • 1.3.1 Aquaculture Monitoring Systems
      • 1.3.1.1 Remote Sensing and Satellite Imaging
      • 1.3.1.2 Internet of Things (IoT) and Smart Sensors
      • 1.3.1.3 Biofloc Technology
    • 1.3.2 Breeding and Genetics
      • 1.3.2.1 Selective Breeding
      • 1.3.2.2 Genetic Engineering
      • 1.3.2.3 Genomic Selection
    • 1.3.3 Water Quality Management
      • 1.3.3.1 Sensor Technology
        • 1.3.3.1.1 Dissolved Oxygen Sensors
        • 1.3.3.1.2 pH and Temperature Sensors
        • 1.3.3.1.3 Multiparameter Probes
      • 1.3.3.2 Biofiltration Systems
        • 1.3.3.2.1 Biological Filters
        • 1.3.3.2.2 Constructed Wetlands
        • 1.3.3.2.3 Algal Turf Scrubbers
      • 1.3.3.3 Automated Water Quality Control Systems
        • 1.3.3.3.1 Automated Monitoring Systems
        • 1.3.3.3.2 Smart Feeding Systems
        • 1.3.3.3.3 AI and Machine Learning Algorithms
  • 1.4 Forestry Technology Innovations
    • 1.4.1 Remote Sensing and GIS
      • 1.4.1.1 Remote Sensing in Forestry
        • 1.4.1.1.1 High-Resolution Satellite Imagery
        • 1.4.1.1.2 Light Detection and Ranging (LiDAR)
        • 1.4.1.1.3 Hyperspectral Imaging
      • 1.4.1.2 GIS in Forestry
        • 1.4.1.2.1 Spatial Data Integration
        • 1.4.1.2.2 Real-Time Data Analysis
        • 1.4.1.2.3 Mobile GIS Applications
    • 1.4.2 Sustainable Forestry Practices
      • 1.4.2.1 Precision Forestry
      • 1.4.2.2 Sustainable Harvesting Techniques
    • 1.4.3 Timber Harvesting Equipment
      • 1.4.3.1 Harvester and Forwarder Automation
      • 1.4.3.2 Precision Forestry (GPS and GIS Integration)
      • 1.4.3.3 Sustainable Harvesting Practices

2 Current State: 2020-2030

  • 2.1 Technological Developments in Crop Production
    • 2.1.1 Advancements in Precision Agriculture
      • 2.1.1.1 Remote Sensing and Satellite Imaging
      • 2.1.1.2 Internet of Things (IoT) and Sensor Networks
      • 2.1.1.3 Drones and UAVs
      • 2.1.1.4 Precision Irrigation Systems
      • 2.1.1.5 Artificial Intelligence (AI) and Machine Learning (ML)
      • 2.1.1.6 Blockchain Technology
    • 2.1.2 Innovations in Crop Genetics
      • 2.1.2.1 CRISPR-Cas9 Gene Editing
      • 2.1.2.2 RNA Interference (RNAi)
      • 2.1.2.3 Genomic Selection
      • 2.1.2.4 Synthetic Biology
      • 2.1.2.5 Marker-Assisted Selection (MAS)
    • 2.1.3 Integration of IoT and AI
      • 2.1.3.1 Precision Farming
      • 2.1.3.2 Smart Irrigation Systems
      • 2.1.3.3 Pest and Disease Detection
      • 2.1.3.4 Soil Health Monitoring
      • 2.1.3.5 Autonomous Farming Machinery
      • 2.1.3.6 Predictive Analytics
  • 2.2 Livestock Production Technologies
    • 2.2.1 Smart Animal Health Monitoring
      • 2.2.1.1 Wearable Sensors
      • 2.2.1.2 Biosensors and Biotelemetry
      • 2.2.1.3 RFID and GPS Tracking
      • 2.2.1.4 Machine Learning and Artificial Intelligence
      • 2.2.1.5 Automated Health Management Systems
      • 2.2.1.6 Drone Technology
    • 2.2.2 Precision Livestock Farming
      • 2.2.2.1 Smart Sensors
      • 2.2.2.2 Wearable Devices
      • 2.2.2.3 Robotic Systems
      • 2.2.2.4 Automated Feeding Systems
      • 2.2.2.5 Genomic Tools
      • 2.2.2.6 Data Analytics and AI
    • 2.2.3 Advances in Livestock Genetics
      • 2.2.3.1 CRISPR-Cas9 Gene Editing
      • 2.2.3.2 Genomic Selection
      • 2.2.3.3 Embryo Transfer and In Vitro Fertilization (IVF)
      • 2.2.3.4 Marker-Assisted Selection (MAS)
      • 2.2.3.5 Epigenetic Modifications
      • 2.2.3.6 Whole-Genome Sequencing (WGS)
  • 2.3 Advances in Aquaculture
    • 2.3.1 IoT and AI in Aquaculture
      • 2.3.1.1 IoT-Based Monitoring Systems
      • 2.3.1.2 AI-Driven Data Analytics
      • 2.3.1.3 Robotics and Automation
      • 2.3.1.4 Blockchain for Supply Chain Transparency
      • 2.3.1.5 Virtual Reality (VR) and Augmented Reality (AR) for Training and Education
    • 2.3.2 Sustainable Feed and Nutrition
      • 2.3.2.1 Alternative Protein Sources
      • 2.3.2.2 Biofloc Technology
      • 2.3.2.3 Precision Nutrition
      • 2.3.2.4 Probiotics and Prebiotics
      • 2.3.2.5 Integrated Multi-Trophic Aquaculture (IMTA)
    • 2.3.3 Innovations in Breeding Techniques
      • 2.3.3.1 Genomic Selection and Marker-Assisted Selection (MAS)
      • 2.3.3.2 CRISPR-Cas9 and Gene Editing
      • 2.3.3.3 Synthetic Biology
      • 2.3.3.4 Epigenetic Modifications
      • 2.3.3.5 Integrated Multi-Trophic Aquaculture (IMTA) and Selective Breeding
  • 2.4 Forestry Technology Trends
    • 2.4.1 Precision Forestry
      • 2.4.1.1 Remote Sensing and GIS
      • 2.4.1.2 Drones
      • 2.4.1.3 AI and Machine Learning
      • 2.4.1.4 IoT and Sensor Networks
    • 2.4.2 Advanced Harvesting Technologies
      • 2.4.2.1 Automated Harvesting Machines
      • 2.4.2.2 Precision Forestry Technologies
      • 2.4.2.3 Sustainable Logging Practices
      • 2.4.2.4 Biomass Harvesting Technologies

3 Comparative Analysis: 2010-2020 vs. 2020-2030

  • 3.1 Technological Progress in Crop Production
    • 3.1.1 Precision Agriculture
    • 3.1.2 GMOs and Hybrid Seeds
    • 3.1.3 Automated Machinery and Equipment
    • 3.1.4 Crop Management Software
    • 3.1.5 Integration of IoT and AI
  • 3.2 Innovations in Livestock Production
    • 3.2.1 Animal Health Monitoring Systems
    • 3.2.2 Genetic Engineering and Breeding Innovations
    • 3.2.3 Livestock Management Software
    • 3.2.4 Smart Animal Health Monitoring
    • 3.2.5 Precision Livestock Farming
    • 3.2.6 Advances in Livestock Genetics
  • 3.3 Advancements in Aquaculture
    • 3.3.1 Aquaculture Monitoring Systems
    • 3.3.2 Breeding and Genetics
    • 3.3.3 Water Quality Management
    • 3.3.4 IoT and AI in Aquaculture
    • 3.3.5 Sustainable Feed and Nutrition
    • 3.3.6 Innovations in Breeding Techniques
  • 3.4 Evolution of Forestry Technology
    • 3.4.1 Remote Sensing and GIS
    • 3.4.2 Sustainable Forestry Practices
    • 3.4.3 Timber Harvesting Equipment
    • 3.4.4 Precision Forestry

4 Achievements and Impacts: Case Study

  • 4.1 Crop Yield Improvements
  • 4.2 Enhanced Livestock Productivity
  • 4.3 Aquaculture Efficiency Gains
  • 4.4 Sustainable Forestry Practices

5 Challenges and Opportunities

  • 5.1 Barriers to Technology Adoption
    • 5.1.1 Crop Production
    • 5.1.2 Livestock
    • 5.1.3 Aquaculture
    • 5.1.4 Forestry
  • 5.2 Government Initiatives and Impacts
  • 5.3 Investment and Funding Trends

6 Recommendation and Conclusion

  • 6.1 Recommendations
    • 6.1.1 Recommendation for Policymakers
    • 6.1.2 Recommendation for Technology Providers
    • 6.1.3 Recommendations for End Users

7 Research Methodology

  • 7.1 Data Sources
    • 7.1.1 Primary Data Sources
    • 7.1.2 Secondary Data Sources

List of Figures

  • Figure 1: Objectives of Technology Adoption in Agriculture
  • Figure 2: History of Agricultural Evolution
  • Figure 3: Major Technological Developments and Adoption in Agriculture Sectors, 2010-2020
  • Figure 4: Major Technological Developments (Ongoing and Expected) in Agriculture Sectors, 2020-2030
  • Figure 5: History of Agricultural Evolution
  • Figure 6: Issues Impacting the Adoption of Precision Agriculture
  • Figure 7: Adoption Rates of Precision Agriculture Technologies by U.S. Farmers for Corn in 2016 and Soybeans in 2018
  • Figure 8: Remote Monitoring of Grape Crops for Disease and Pest Management - Infosys
  • Figure 9: Automated Irrigation System Development - Krish Compusoft Services (KCS)
  • Figure 10: Global Area of GM Crops, 2000-2017, Million Hectare
  • Figure 11: Crop Species with More than Five Landraces Documented in CONECT-e
  • Figure 12: Agriculture Tractor Registrations in Europe (by Year), 2016-2023
  • Figure 13: Transforming Farm Management at MacField Farms with Conservis Technology
  • Figure 14: U.S. Broilers Production, 2002-2022
  • Figure 15: Flow Diagram of Animal Health Monitoring System
  • Figure 16: Evolution of Genetically Modified Livestock
  • Figure 17: Case Study: Livestock Management Software: AgriERP and Rombola Family Farms
  • Figure 18: Case Study: AquaTech Solutions and BlueWave Aquaculture
  • Figure 19: Case Study: Smart Aqua Systems and GreenWave Aquaculture
  • Figure 20: Process of Technology Adoption in Forestry
  • Figure 21: Components of Remote Sensing and GIS in Forestry
  • Figure 22: Contribution of Innovative Technologies in Sustainable Forestry
  • Figure 23: Adoption of Precision Agriculture in the U.S. Farms, 2022-2023
  • Figure 24: Global Agriculture Goals Achieved through Smart Farming
  • Figure 25: Comparison between Conventional Breeding and Genetic Engineering
  • Figure 26: Reduction of Input Resources - Zerynth S.p.A.
  • Figure 27: Enhancing Sustainability and Traceability in Hazelnut Production
  • Figure 28: U.S. Broilers Production, 2024-2030
  • Figure 29: Case Study: Smart Livestock Management With IoT Solutions
  • Figure 30: Remote Sensing and GIS in Precision Forestry: Arbonaut
  • Figure 31: Forest Management with Digital Twin Technology
  • Figure 32: Forestry Monitoring and Management Using Remote Sensing and GIS Technology
  • Figure 33: Roadmap in Forestry Technology Innovations
  • Figure 34: Case Study: Implementation of Smart Technologies in Forestry Production: Stora Enso
  • Figure 35: Crop Yield Improvements Case Study
  • Figure 36: Enhanced Livestock Productivity Case Study
  • Figure 37: Aquaculture Efficiency Gains Case Study
  • Figure 38: Sustainable Forestry Practices Case Study
  • Figure 39: Investments in Agriculture Technologies (by Year-on-Year), $Billion, 2019-2023
  • Figure 40: Agriculture Technology Transition Assesment (2010-2030): Research Methodology

List of Tables

  • Table 1: Key Technologies and Providers
  • Table 2: Development and Adoption of Precision Agriculture Technologies, 2010-2020
  • Table 3: Development and Adoption of GMOs and Hybrid Seeds, 2010-2020
  • Table 4: Development and Adoption of Automated Machinery and Equipment, 2010-2020
  • Table 5: Development and Adoption of Technologies in Crop Management Software, 2010-2020
  • Table 6: Key Technologies and Providers
  • Table 7: Key Livestock Management Providers
  • Table 8: Development and Adoption of Technologies in Animal Health Monitoring, 2010-2020
  • Table 9: Development and Adoption of Technologies in Genetic Engineering and Breeding Innovations, 2010-2020
  • Table 10: Development and Adoption of Technologies in Livestock Management Software, 2010-2020
  • Table 11: Development and Adoption of Technologies in Aquaculture Monitoring Systems, 2010-2020
  • Table 12: Development and Adoption of Technologies in Breeding and Genetics, 2010-2020
  • Table 13: Development and Adoption of Technologies in Water Quality Management, 2010-2020
  • Table 14: Key Technologies and Providers
  • Table 15: Key Agroforestry Technology Providers
  • Table 16: Development and Adoption of Technologies in Remote Sensing and GIS, 2010-2020
  • Table 17: Development and Adoption of Technologies in Sustainable Forestry Practices, 2010-2020
  • Table 18: Development and Adoption of Technologies in Sustainable Forestry Practices, 2010-2020
  • Table 19: Key Precision Agriculture Companies
  • Table 20: Expected Technological Developments and Advancements in Precision Agriculture, 2020-2030
  • Table 21: Expected Technological Developments and Innovations in Crop Genetics, 2020-2030
  • Table 22: Expected Technological Developments and Innovations in the Integration of IoT and AI, 2020-2030
  • Table 23: Expected Technological Developments and Innovations in Smart Animal Health Monitoring, 2020-2030
  • Table 24: Expected Technological Developments and Innovations in Precision Livestock Farming, 2020-2030
  • Table 25: Expected Technological Developments and Innovations in Advances in Livestock Genetics, 2020-2030
  • Table 26: Expected Technological Developments and Innovations in IoT and AI for Aquaculture, 2020-2030
  • Table 27: Expected Technological Developments and Innovations in Sustainable Feed and Nutrition, 2020-2030
  • Table 28: Expected Technological Developments and Innovations in Breeding Techniques, 2020-2030
  • Table 29: Expected Technological Developments and Innovations in Precision Forestry, 2020-2030
  • Table 30: Expected Technological Developments and Innovations in Precision Forestry, 2020-2030
  • Table 31: Technological Progress in Crop Production (2010-2030)
  • Table 32: Technological Progress in Livestock Production (2010-2030)
  • Table 33: Technological Progress in Livestock Production (2010-2030)
  • Table 34: Technological Progress in Livestock Production (2010-2030)
  • Table 35: Key Government Initiatives/Programs