EAGER: Computational Agroecology: A Systems Approach

最近的网络验证工作必须同时处理大型网络的硬件和软件元素的异构性，以复杂的方式相互作用，并在网络规划和管理中的实际使用的缩放验证和合成。 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，世界上只有7种作物生产了全球80%的卡路里，这种情况在需求不断增加和压力不断变化的情况下是不安全的。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，以统一数字农业的不同方法，旨在提高粮食系统的生产力和安全性，同时推进大规模农业生态系统计算建模的最新技术。 由于这种大规模，我们将开发新的技术来扩展网络验证，特别是在只有近似数据的情况下。 本研究中开发的创新将适用于大规模网络验证、规划和配置。具体来说，本项目将引入数字农业的计算框架，涵盖精准农业和农业生态学。该计算框架将继续使用基于网络验证的时空状态空间表示法来分析、模拟和理解农业生态系统。该方法可以考虑农业生态设计和管理系统，包括作物和种植系统的复杂混合，这在传统方法中很少考虑;它同时使以前难以理解的农业生态方法的严格分析成为可能。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基本指导。 该项目将包括一个概念性的状态空间框架，称为农业生态过渡函数和一个实用的软件系统框架，称为计算农业生态。除了简单地推进农业生态学的理解，该项目将通过探索状态空间探索来推进网络系统研究，例如在网络验证中，比以前大得多的规模，并通过这样做来考虑在复杂的物理环境中应用新型网络传感和驱动系统。 该框架将通过新的抽象来实例化，用于对网络基础设施进行编程，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的人类实践和技术。 这项研究的成果将应用于网络系统研究的核心领域;具体来说，这项工作将通过改进近似和混叠，通过将开发的状态空间框架，使网络验证和综合的规模得到改善。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.