SI Network

以可持续的方式养活不断增长的全球人口是人类面临的最紧迫的挑战之一。一个关键的知识差距是如何提高现有农田的作物和牲畜产量，同时保持或加强所有生态系统服务所依赖的自然资本。这种“可持续集约化”（SI）的理念现在已被嵌入到RCUK的战略目标中，在英国政府政策中获得了牵引力，并吸引了大量投资。然而，尽管这种水平的活动，SI仍然是一个有争议的概念之间的一些部分的研究界。此外，还存在一种风险，即在从事这一领域的各种研究界缺乏定义和协调，可能导致重复、浪费努力和错失机会。存在着紧张关系和悬而未决的问题：特别是需要调和围绕开发或保护自然资源的对立观点。SI有时被视为一个自然科学问题，但需要确保农村社区的经济和社会福利，而且必须让土地管理者参与进来，他们将执行提供SI的措施。因此，显然需要促进跨学科工作的发展，并使研究人员和其他利益相关者的不同组合能够分享想法，技能，专业知识和能力，以最大限度地提高英国应对当前和未来SI挑战的能力。可持续强化研究网络（SIRN）将建立在现有的，自下而上的倡议基础上，由英国研究人员和科学家开发，更好地整合RCUK社区，并更充分地利用基础设施，专业知识和能力来应对SI带来的挑战。建立SIRN将使社区能够进一步发展SI的清晰和一致的共同愿景，并实现切实的利益，这将提供基础发现科学，改善业务绩效，并支持社会各阶层的循证决策。到目前为止，社区牵头的讲习班已经制定了一份研究宣言，并确定了优先问题并进行了排序（见www.sustainableintensification.org.uk上的报告）。SIRN将在这些基础上再接再厉，为更广泛的研究界提供多种机会，以掌握这些（和新的）领域，将它们与不同利益攸关方的需求联系起来，评估知识差距，并确定填补这些差距的机制。我们对网络的共同愿景是建立一个平台，确保多样化的研究社区能够轻松地充分参与，以便：优化对生物科学、社会科学、经济学和农业技术领域的社会科学的重大投资，（ii）响应促进跨学科的方法，以通知和响应RCUK倡议和英国政府和行业的需求;（iii）与新的研究领域（如合成生物学，预测生物学，分子技术），更成熟的研究领域（如，农业生态学、植物育种、土壤科学）和新的工作方式（如大数据）。

Feeding an ever growing global population in a sustainable way is one of the most pressing challenges facing humanity. A key knowledge gap is how to increase crop and livestock yields from existing farmland while maintaining or enhancing the natural capital on which all ecosystem services depend. This idea of 'sustainable intensification' (SI) is now embedded into strategic goals across RCUK, has gained traction within UK government policies and has attracted considerable investment. However, despite this level of activity, SI remains a controversial concept amongst some parts of the research community. Further, there is a risk that a lack of definition and co-ordination across the diverse research community that are engaging with this area could result in duplication, wasted effort and missed opportunities. There are tensions and unresolved issues: in particular the need to reconcile the opposing views surrounding exploiting or conserving natural resources. SI is sometimes seen as a natural science issue but there is a need to ensure the economic and social welfare of rural communities and it is vital to engage the managers of land who will implement measures to deliver SI. There is thus a clear need to facilitate the development of interdisciplinary working and enable different combinations of researchers and other stakeholders to share ideas, skills, expertise and capabilities in order to maximise the UK's capacity to meet the diverse current and future SI challenges.The Sustainable Intensification Research Network (SIRN) will build on an existing, bottom-up initiatives, developed by UK researchers and scientists, to better integrate RCUK communities and enable fuller use of infrastructure, expertise and capabilities to address the challenges posed by SI. Establishing SIRN will allow the community to further develop a clear and coherent, shared vision of SI and enable the realisation of tangible benefits that will deliver fundamental discovery science, improved business performance and support evidence-based policy making to all levels of society. To date community-led workshops have already developed a research manifesto and identified and ranked priority issues (see report at www.sustainableintensification.org.uk). SIRN will build on these foundations by providing multiple opportunities for the wider research community to take ownership of these (and new) areas, link them to the needs of different stakeholders and appraise knowledge gaps and identify mechanisms to fill them. Our shared vision for the network is to build a platform that will ensure the diverse research community is able to easily become fully engaged in order to: (i) optimise major investment into SI across the biological sciences, social sciences, economics and agri-tech, (ii) respond facilitate a across- disciplinary approach es to inform and respond to RCUK initiatives and UK Gov and Industry needs and (iii) develop effective links with new research areas (e.g. synthetic biology, predictive biology, molecular technologies), more established research areas (eg. agro-ecology, plant breeding, soil science) and new ways of working (e.g. big data).