EAGER: Computational Agroecology: A Systems Approach

最近关于网络验证的工作必须同时处理以复杂方式相互作用的硬件和软件元素的大型网络的异质性，以及在网络规划和管理中实际使用的规模验证和综合。这些挑战反映了将计算连贯地应用于农业新挑战的挑战。在过去的80年里，生产全球80%热量的作物急剧减少到只有7种，鉴于需求的增加和压力的变化，这种情况是不安全的。该项目将引入一个新的计算框架，扩展和扩展最近的网络研究技术，统一不同的数字农业方法，旨在提高粮食系统的生产力和安全性，同时推进大规模计算农业生态系统建模的最新技术。由于这种大规模，我们将开发用于扩展网络验证的新技术，特别是在只有近似数据可用的设置中。在本研究中发展的创新将适用于大规模网络的验证、规划和供应。具体而言，该项目将为数字农业引入一个包含精准农业和农业生态学的计算框架。该计算框架将使用基于网络验证的时空-状态-空间表示，对一块土地的无限可能配置及其上的生物地球化学元素进行分析、模拟和理解。这种方法可以考虑农业生态设计和管理制度，包括传统方法中很少考虑的作物和种植制度的复杂混合；同时，它可以对以前难以理解的农业生态方法进行严格的分析。这一新框架将为人类管理的大片土地面临的重大变化提供必要的指导。该项目将包括一个名为“生态农业过渡函数”的概念状态空间框架和一个名为“计算生态农业”的实用软件系统框架。除了简单地推进对农业生态的理解之外，该项目还将通过在比以前大得多的规模上探索网络验证等状态空间探索，并考虑在复杂物理环境中应用新型的传感和驱动网络系统，来推进网络系统的研究。该框架将通过编程网络基础设施的新抽象来实例化，以探索传感和驱动方面的新工程技术，包括尚未具有物理实例化的人类实践和技术。本研究成果将应用于网络系统研究的核心领域；具体来说，这项工作将通过将开发的状态空间框架改进近似和混叠，从而改进网络验证和综合的规模。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Recent work on network verification has had to deal simultaneously with the heterogeneity of large networks of hardware and software elements that interact in complex ways and with scaling verification and synthesis for practical use in network planning and management. These challenges mirror the challenges in applying computing coherently to new challenges in agriculture. The last eighty years have seen a dramatic narrowing to just 7 crops that produce 80% of global calories, a situation that is insecure given increasing demands and changing pressures. This project will introduce a new computational framework extending and expanding recent networking research techniques to unify the disparate approaches to Digital Agriculture, with an aim to yield both greater productivity and greater security in the food system while advancing the state of the art in modeling agroecosystems computationally at large scale. As a result of this large scale, we will develop new techniques for scaling network verification, especially in settings where only approximate data is available. The innovations developed in this research will be applicable back to large-scale network verification, planning, and provisioning.Specifically, this project will introduce a computational framework for Digital Agriculture that subsumes both precision agriculture and agroecology. This computational framework will proceed to root the analysis, simulation, and understanding of agroecosystems using a network-verification-based space-time state-space representation of the infinite possible configurations of a piece of land and the biogeochemical elements on it. This approach enables consideration of agroecological designs and systems of management, including complex mixtures of crops and cropping systems, that are seldom considered in conventional approaches; it simultaneously enables rigorous analysis of formerly inscrutable agroecological methods. This new framework will provide essential guidance for the critical changes facing vast human-managed lands. This project will consist of a conceptual state-space framework called Agroecological Transition Functions and a practical software systems framework called Computational Agroecology. Beyond simply advancing agroecological understanding, this project will advance networked systems research by exploring state-space exploration, such as in network verification, at much larger scale than before, and by doing so considering the application of new types of networked systems of sensing and actuation in a complex physical environment. This framework will be instantiated through new abstractions for programming cyberinfrastructure to explore new engineered technologies in sensing and actuation, including human practices and technologies that do not yet have physical instantiations. The outputs of this research will apply back to core areas of networked systems research; specifically, this work will enable improved scaling of network verification and synthesis through improvements in approximation and aliasing via the state-space framework that will be developed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.