A scalable framework for quantifying field-level agricultural carbon outcomes
A scalable framework for quantifying field-level agricultural carbon outcomes
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DOI：
10.1016/j.earscirev.2023.104462
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发表时间：
2023-07-26
影响因子：
12.1
通讯作者：
Yang, Shang-Jen
中科院分区：
文献类型：
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作者：
Guan, Kaiyu;Jin, Zhenong;Yang, Shang-Jen
农业贡献了全球温室气体(GHG)排放量的近四分之一，这激发了人们对采用某些耕作做法的兴趣，这些耕作做法有可能减少温室气体排放或将碳封存在土壤中。相关的温室气体排放(包括N2O和CH4)和土壤碳储量的变化在这里被定义为“农业碳结果”。农业碳排放结果的准确量化是实现农业减排的基础，但现有的碳排放结果衡量方法(包括直接测量、排放系数和基于过程的建模)无法实现必要的准确性和可扩展性，以支持对这些碳结果进行可信、可验证和具有成本效益的衡量和改进。在这里，我们提出了一个基础的和可扩展的框架来量化农田层面的碳结果，该框架基于农业生态系统的整体碳平衡：农业生态系统碳结果1/4环境(E)和磅；管理(M)和磅；作物(C)。在全面审查了与现有方法相关的科学挑战以及它们在成本和准确性之间的权衡之后，我们提出，在农业用地上量化田间层面的碳结果的最可行的途径是通过有效地整合各种方法(例如，不同的观测、传感器/现场数据和建模)，被定义为“系统系统”解决方案。这种“系统系统”解决方案应同时包括以下几个部分：(1)可扩展的地面真实数据收集和环境变量(E)、管理实践(M)和作物状况(C)的跨尺度监测；(2)先进的建模和必要的过程，以支持碳结果的量化；(3)系统模型-数据融合(MDF)，即在每个当地农田水平综合监测数据和模型的稳健和有效的方法；(4)高计算效率和人工智能(AI)，以低成本扩展到数百万个单独的领域；以及(5)健壮的多层验证系统和基础设施，以确保解决方案的保真度和真正的可扩展性，即解决方案在所有目标领域以公认的精度稳健执行的能力。在这方面，我们在这里提供了详细的科学基础、当前的进展和未来的研发(R&D)优先事项，以实现“系统系统”解决方案的不同组成部分，从而完成环境管理xCrop框架，以量化田间层面的农业碳结果。
Agriculture contributes nearly a quarter of global greenhouse gas (GHG) emissions, which is motivating interest in adopting certain farming practices that have the potential to reduce GHG emissions or sequester carbon in soil. The related GHG emission (including N2O and CH4) and changes in soil carbon stock are defined here as "agricultural carbon outcomes". Accurate quantification of agricultural carbon outcomes is the basis for achieving emission reductions for agriculture, but existing approaches for measuring carbon outcomes (including direct measurements, emission factors, and process-based modeling) fall short of achieving the required accuracy and scalability necessary to support credible, verifiable, and cost-effective measurement and improvement of these carbon outcomes. Here we propose a foundational and scalable framework to quantify field-level carbon outcomes for farmland, which is based on the holistic carbon balance of the agroecosystem: Agroecosystem Carbon Outcomes 1/4 Environment (E) & POUND; Management (M) & POUND; Crop (C). Following a comprehensive review of the scientific challenges associated with existing approaches, as well as their tradeoffs between cost and accuracy, we propose that the most viable path for the quantification of field-level carbon outcomes in agri-cultural land is through an effective integration of various approaches (e.g. diverse observations, sensor/in-situ data, and modeling), defined as the "System-of-Systems" solution. Such a "System-of-Systems" solution should simultaneously comprise the following components: (1) scalable collection of ground truth data and cross-scale sensing of environment variables (E), management practices (M), and crop conditions (C) at the local field level; (2) advanced modeling with necessary processes to support the quantification of carbon outcomes; (3) sys-tematic Model-Data Fusion (MDF), i.e. robust and efficient methods to integrate sensing data and models at each local farmland level; (4) high computation efficiency and artificial intelligence (AI) to scale to millions of in-dividual fields with low cost; and (5) robust and multi-tier validation systems and infrastructures to ensure solution fidelity and true scalability, i.e. the ability of a solution to perform robustly with accepted accu-racy on all targeted fields. In this regard, we provide here the detailed scientific rationale, current progress, and future research and development (R & D) priorities to achieve different components of the "System-of-Systems" solution, thus accomplishing the EnvironmentxManagementxCrop framework to quantify field-level agricul-tural carbon outcomes.