可持续农业和环境管理的新兴技术框架

Bhupendra Chaudhary , Vijay Kumar
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引用次数: 5

摘要

全球人口在不断增加,而由于城市定居和工业发展的原因,农业用地资源在不断减少。这无疑给农业部门增加年产量带来了压力,尤其是在气候变化的情况下。为满足消费者对食品、饲料和纤维日益增长的需求,到2050年,农作物产量将增长70%。为此目的,在过去几十年中,现代农业可利用的资源主要是通过传统的遗传学方法加以利用的。目前,最先进的生物技术的出现正在解决这些领域的挑战,以实现持续的“农业成功”。在这里,我们通过持久的联盟来考虑资源丰富的农业实践:1)分子育种、基因工程(GE)和基因编辑技术,以提高作物产量,减少生物/非生物胁迫介导的农业损失;2)环境弹性,包括使我们生活的各个方面可持续发展的解决方案。我们还提供了全球工程作物事件的生态亲和性概述;此外,还将部署生物技术方法,以应对可再生资源(包括生物质到生物燃料)的可持续性挑战,以及生物修复过程。此外,人工智能(AI)和机器学习(ML)支持的作物挑战解决方案的整合正在彻底改变现有的农业资源,并为下一代作物管理和环境适应能力提供路线图。为了在持续变化的气候条件下提高农业生产,实现环境可持续性目标,本文讨论了各种技术领域的创新,并为未来的研究发展确定了研究空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emerging Technological Frameworks for the Sustainable Agriculture and Environmental Management

The global human population is constantly increasing whereas the agricultural land resources are declining for reasons of urban settlements and industrial growth. This has certainly strained the agricultural sector for increased annual yields, especially under the changing climate. To accomplish the rising demand of consumers’ for food, feed and fiber, up to 70% upsurge in the crop production is farsighted by 2050. For this purpose, the accessible resources of the modern agriculture have been harnessed mainly through conventional genetics approaches in the past decades. Presently, the advent of state-of-the-art biotechnologies is addressing such field challenges for continuous ‘agricultural success’. Here, we deliberate about the resourceful agricultural practices by the durable alliance of i) molecular breeding, genetic-engineering (GE) and gene-editing technologies for increased crop yields and decreased biotic/abiotic stress-mediated agricultural losses, and ii) environment resilience including solutions to make all aspects of our lives sustainable. We also provide an overview of the ecological affability of engineered crop events worldwide; and also the deployment of biotechnology methodologies to address challenges in the sustainability of renewable resources including biomass to biofuel, and bioremediation processes. Further, the integration of artificial intelligence (AI) and machine learning (ML)-empowered solutions to crop challenges is revolutionizing the prevailing agri-resources and providing a roadmap to the next-generation crop management and environment resilience. In order to enhance the agricultural production under continuously changing climate, and to achieve the environmental sustainability goals, various technological field-innovations have been discussed, and research gaps are identified for future research developments.

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