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The slowing of agricultural productivity growth globally over the past two decades has brought a new urgency to detect its drivers and potential solutions. We show that air pollution, particularly surface ozone (O3), is strongly associated with declining agricultural total factor productivity (TFP) in China. We employ machine learning algorithms to generate estimates of high-resolution surface O3 concentrations from 2002 to 2019. Results indicate that Chinas O3 pollution has intensified over this 18-year period. We coupled these O3 estimates with a statistical model to show that rising O3 pollution during nonwinter seasons has reduced agricultural TFP by 18% over the 2002-2015 period. Agricultural TFP is projected to increase by 60% if surface O3 concentrations were reduced to meet the WHO air quality standards. This productivity gain has the potential to counter expected productivity losses from 2°C warming.

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 = Environment (E) × Management (M) × 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 agricultural 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) systematic 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 individual 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 accuracy 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 Environment×Management×Crop framework to quantify field-level agricultural carbon outcomes.

Societal Impact Statement: Transformative agricultural strategies like agrivoltaics (AV) are essential for addressing the pressing global issues of sustainable energy and food production in a changing climate. Conservation‐agrivoltaics (Conservation‐AV) provides the potential to meet these needs while reinforcing natural resources and protecting the environment. It could enhance the ecological benefits of AV by improving soil health and biodiversity. It could create economic opportunities for farmers and increase the resilience and diversity of food crops under changing climate conditions. Furthermore, it could inform stakeholders about the benefits and challenges of implementing conservation agriculture management practices (CAMP) in AV and encourage further exploration and adoption of this innovative approach. Summary: Transformative strategies in agriculture are needed to address urgent global challenges related to energy and food production while reinforcing natural resources and the environment. Agrivoltaics (AV) has emerged in the past decade as one solution to this fundamental challenge of improving energy and food security. AV is defined as the co‐location of solar photovoltaic (PV) panels and crops on the same land to optimize food and energy production simultaneously and sustainably. Here, we propose that AV, together with conservation agriculture management practices (CAMP) strategies can help to intensify food security and energy production while reinforcing natural resources and the environment. Our main assertions in this opinion article are that: (1) AV systems need to overcome several agronomical, environmental, and ecological challenges to intensify food and energy production sustainably; (2) CAMP applied to AV systems can preserve the environment and ensure climate‐resilient food production; (3) implementation of CAMP in AV can lead to long‐term carbon sequestration, lower greenhouse gas emissions, and maintain or increase crop yields while preserving soil health and biodiversity; and (4) adoption of CAMP in AV can bring economic benefits, although challenges need to be overcome. This opinion article proposes a new ecosystem approach to integrate renewable energy and sustainable food production and encourages research on the effects of CAMP on AV systems. [ABSTRACT FROM AUTHOR]