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市場調查報告書
商品編碼
1911567
農業基因組學市場規模、佔有率和成長分析(按技術、產品類型、應用、最終用戶和地區分類)—2026-2033年產業預測Agriculture Genomics Market Size, Share, and Growth Analysis, By Technology (Next-Generation Sequencing, Real-Time PCR ), By Product Type, By Application, By End-User, By Region - Industry Forecast 2026-2033 |
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預計到 2024 年,全球農業基因組學市場規模將達到 36.1 億美元,到 2025 年將達到 39.6 億美元,到 2033 年將達到 81.8 億美元,在預測期(2026-2033 年)內複合年成長率為 9.5%。
農業基因組學市場正經歷顯著成長,這主要得益於供應鏈管理、需求預測以及作物改良舉措的定向投資等方面的進步。識別新興市場趨勢並增強區域觀點,使企業能夠利用數據建模結果,在種子基因組學和畜牧遺傳學領域做出明智的決策。這種方法有助於擴大市場滲透率,並促進永續成長。此外,人工智慧的整合在推動創新和增強競爭力方面發揮關鍵作用,並已成為充滿活力的農業基因組學領域長期收入成長的關鍵催化劑。適應這些發展趨勢將使企業能夠更好地應對不斷變化的市場格局的複雜性。
推動全球農業基因組學市場發展的因素
全球對糧食安全、永續農業實踐和氣候適應型農業日益成長的關注,正顯著推動農業領域對基因組解決方案的需求。先進的定序和生物資訊技術的創新,使得育種決策更加精準,抗病性得以識別,產量最佳化也得到了提升。因此,這些進步正在促進基因組應用的廣泛普及,創造了巨大的成長機遇,並引導各個農業領域的策略性舉措。在應對環境挑戰的同時提高農業生產力,凸顯了基因組指南在塑造農業未來方面將發揮的關鍵作用。
全球農業基因組學市場限制因素
全球農業基因組學市場面臨著許多挑戰,其中定序技術和生物資訊分析的高昂成本限制了其應用,尤其是在發展中地區。此外,缺乏熟練的人力資源和必要的基礎設施也可能進一步阻礙市場成長和服務在各地區的擴展。為了促進該領域的發展,至關重要的是實施成本降低策略和知識轉移舉措,以提高基因組學技術的可及性,並促進其在農業中的有效應用。除非這些障礙得到解決,否則農業基因組學的潛力可能仍將無法充分發揮。
全球農業基因組學市場趨勢
全球農業基因組學市場正經歷顯著的轉變,朝著人工智慧增強的感測器融合和感知系統發展,這些系統利用先進的機器學習演算法來解讀大規模多體學資料集。這種創新方法有助於將基因型轉化為表現型,從而能夠基於基因組圖譜準確預測植物的性能。因此,育種者可以加快選擇過程,縮短育種週期,最終提高育種結果的統計置信度。人工智慧在農業基因組學中的應用不僅簡化了工作流程,還有助於提高作物的抗逆性和產量,使其成為應對全球糧食安全挑戰的關鍵要素。
Global Agriculture Genomics Market size was valued at USD 3.61 Billion in 2024 and is poised to grow from USD 3.96 Billion in 2025 to USD 8.18 Billion by 2033, growing at a CAGR of 9.5% during the forecast period (2026-2033).
The agriculture genomics market is experiencing significant growth driven by advancements in supply chain management, demand forecasting, and targeted investment in crop improvement initiatives. By identifying emerging market trends and enhancing regional perspectives, companies can leverage data modeling outcomes to make informed decisions regarding seed genomics and livestock genetics. This approach facilitates broader market penetration and fosters sustainable growth. Furthermore, the integration of artificial intelligence is becoming instrumental in fostering innovation and enhancing competitiveness, positioning itself as a crucial catalyst for long-term revenue expansion in the dynamic agriculture genomics sector. As businesses adapt to these developments, they are better equipped to navigate the complexities of this evolving market landscape.
Top-down and bottom-up approaches were used to estimate and validate the size of the Global Agriculture Genomics market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Global Agriculture Genomics Market Segments Analysis
Global Agriculture Genomics Market is segmented by Technology, Product Type, Application, End-User and region. Based on Technology, the market is segmented into Next-Generation Sequencing (NGS), Real-Time PCR (qPCR), Microarrays, Gene Editing and SNP Genotyping / Marker-Assisted Selection. Based on Product Type, the market is segmented into Sequencing Instruments, Genotyping Equipment, Kits & Reagents, Software & Services and Other Equipment. Based on Application, the market is segmented into Crop / Plant Breeding, Livestock / Animal Breeding, Trait Analysis and Disease Diagnostics in Agriculture. Based on End-User, the market is segmented into Agricultural Biotechnology Companies, Research / Academic Institutes, Government / Public Sector and Agribusiness Firms. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.
Driver of the Global Agriculture Genomics Market
The growing global emphasis on food security, sustainable farming practices, and climate-resilient agriculture has significantly boosted the demand for genomic solutions in the agricultural sector. Innovations in advanced sequencing and bioinformatics technologies enable more accurate breeding decisions, the identification of disease resistance, and improved yield optimization. As a result, these advancements enhance the penetration of genomic applications, unlock substantial growth opportunities, and inform strategic initiatives within various agricultural domains. The drive towards enhancing agricultural productivity while addressing environmental challenges illustrates the vital role of genomics in shaping the future of agriculture.
Restraints in the Global Agriculture Genomics Market
The Global Agriculture Genomics market faces significant challenges due to the high expenses associated with sequencing technologies and bioinformatics analysis, which limit their adoption, especially in developing regions. Additionally, inadequate access to skilled personnel and essential infrastructure can further impede market growth and the expansion of services in various areas. To foster progress in this field, it is crucial to implement cost-reduction strategies and knowledge-transfer initiatives that can enhance accessibility and facilitate the effective use of genomic technologies in agriculture. Without addressing these barriers, the potential of agricultural genomics may remain largely untapped.
Market Trends of the Global Agriculture Genomics Market
The Global Agriculture Genomics market is experiencing a significant trend towards AI-enhanced sensor fusion and perception systems, which leverage advanced machine learning algorithms to interpret extensive multi-omics datasets. This innovative approach facilitates genotype-to-phenotype translation, allowing for accurate predictions of plant performance based on genomic profiles. As a result, breeders can expedite selection processes and shorten breeding cycles, ultimately bolstering their statistical confidence in breeding outcomes. This integration of AI in agriculture genomics not only streamlines workflows but also enhances crop resilience and yields, positioning it as a vital component in meeting global food security challenges.