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Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

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.

Cropland carbon budget depicts the amount of carbon flowing in and out of agroecosystems and the changes in carbon stocks of soil and living biomass during the same period. Soil carbon credit is the additional change in soil carbon stock under certain farming practices compared with the business-as-usual practices. Accurately calculating cropland carbon budget and soil carbon credit is critical to assessing climate change mitigation potential in agroecosystems. The calculation of cropland carbon budget and soil carbon credit is sensitive to local soil and climatic conditions, especially initial soil organic carbon (SOC) stock, which is determined by both SOC concentration (SOC%) and bulk density (Bulk_Density). SOC stock data are either from soil sampling or gridded public survey data. In agroecosystem models, SOC stock data are a key model input for quantifying cropland carbon budget and soil carbon credit. However, various types and degrees of uncertainties exist in SOC stock datasets, which propagate to the quantification of SOC stock change. In particular, a large discrepancy is found in two widely used SOC stock datasets — Rapid Carbon Assessment dataset (RaCA) and Gridded Soil Survey Geographic Database (gSSURGO) — in the U.S. Midwest, with a relative difference (quantified using Normalized Root Mean Square Error, NRMSE) of 48.0% for 0–30 cm SOC stock between the two datasets. It remains largely unclear how uncertainty in SOC stocks affects the calculation of cropland carbon budget and soil carbon credit. To address this question, we used a well-validated process-based agroecosystem model, ecosys, to assess the impacts of SOC stock uncertainty on carbon budget and soil carbon credit calculation in the U.S. Midwestern corn-soybean rotation systems. Our results reveal the following findings: (1) A sizable discrepancy exists in simulated cropland carbon budget between using gSSURGO and using RaCA for their SOC% and Bulk_Density as model inputs, with a Pearson correlation coefficient (r) of only 0.4 for simulated change of SOC stock (ΔSOC) using these two different soil datasets. (2) Simulated cropland carbon budget components were more sensitive to initial SOC% than to Bulk_Density. For example, the upper and lower quartiles of multi-year averaged ΔSOC were −29.8 and 4.8 gC/m2/year for the selected counties respectively, with an uncertainty of 13.7 and 0.7 gC/m2/year induced by uncertainties in initial SOC% and Bulk_Density, respectively. (3) Both simulated ΔSOC and its uncertainty were negatively correlated with initial SOC%, whereas ΔSOC was negatively correlated with air temperature, and ΔSOC uncertainty was positively correlated with air temperature. (4) The uncertainty of calculated soil carbon credits was much smaller compared with the uncertainty of calculated absolute carbon budgets assuming the same SOC stock uncertainty level in the inputs. Specifically, in our assessment comparing planting cover crops vs no cover crop, the uncertainty of calculated soil carbon credits induced by initial SOC% uncertainty was less than 4% (relative to the quantified value of the soil carbon credits) for 90% of the cases. Our analysis highlights that high accuracy measurement of SOC% as inputs is needed for the calculation of cropland carbon budgets; however, soil carbon credit quantification is much less sensitive to the initial SOC% inputs, and the current publicly available soil datasets (e.g., gSSURGO) are largely suitable for the calculation of soil carbon credits.

In this paper, the inhibitory effect of various nanomaterials on the growth of Aspergillus niger was studied. Among them, copper nanorods had the most obvious inhibitory effect on the growth of Aspergillus niger. The phase of copper nanorods was modified by chitosan, and its inhibitory effect on the expansion of Aspergillus niger was measured. 1. Preparation of copper nanorods and chitosan@copper nanorods: Copper nanorods with a diameter of about 300–350 nm and a length of about 100–800 nm were prepared by the liquid-phase reduction method. The chitosan solution was prepared by using the characteristics of chitosan dissolved in dilute acid to prepare chitosan@copper nanorods and modify the phase of copper nanorods. 2. Determination of the inhibitory effect of various copper nanomaterials on the growth of Aspergillus niger, including Cuprous Oxide nanoparticles, copper nanorods, nano copper oxide, and copper hydroxide, which have certain inhibitory effects on the growth of Aspergillus niger. Among them, copper nanorods have a better effect. On this basis, chitosan@copper nanorods are obtained by modifying the phase of copper nanorods with chitosan. The measured antibacterial effect is that the EC50 value is 344 mg/L. [ABSTRACT FROM AUTHOR]

Cover crops have been reported as one of the most effective practices to increase soil organic carbon (SOC) for agroecosystems. Impacts of cover crops on SOC change vary depending on soil properties, climate, and management practices, but it remains unclear how these control factors affect SOC benefits from cover crops, as well as which management practices can maximize SOC benefits. To address these questions, we used an advanced process‐based agroecosystem model, ecosys, to assess the impacts of winter cover cropping on SOC accumulation under different environmental and management conditions. We aimed to answer the following questions: (1) To what extent do cover crops benefit SOC accumulation, and how do SOC benefits from cover crops vary with different factors (i.e., initial soil properties, cover crop types, climate during the cover crop growth period, and cover crop planting and terminating time)? (2) How can we enhance SOC benefits from cover crops under different cover crop management options? Specifically, we first calibrated and validated the ecosys model at two long‐term field experiment sites with SOC measurements in Illinois. We then applied the ecosys model to six cover crop field experiment sites spanning across Illinois to assess the impacts of different factors on SOC accumulation. Our modeling results revealed the following findings: (1) Growing cover crops can bring SOC benefits by 0.33 ± 0.06 MgC ha−1 year−1 in six cover crop field experiment sites across Illinois, and the SOC benefits are species specific to legume and non‐legume cover crops. (2) Initial SOC stocks and clay contents had overall small influences on SOC benefits from cover crops. During the cover crop growth period (i.e., winter and spring in the US Midwest), high temperature increased SOC benefits from cover crops, while the impacts from larger precipitation on SOC benefits varied field by field. (3) The SOC benefits from cover crops can be maximized by optimizing cover crop management practices (e.g., selecting cover crop types and controlling cover crop growth period) for the US Midwestern maize–soybean rotation system. Finally, we discussed the economic and policy implications of adopting cover crops in the US Midwest, including that current economic incentives to grow cover crops may not be sufficient to cover costs. This study systematically assessed cover crop impacts for SOC change in the US Midwest context, while also demonstrating that the ecosys model, with rigorous validation using field experiment data, can be an effective tool to guide the adaptive management of cover crops and quantify SOC benefits from cover crops. The study thus provides practical tools and insights for practitioners and policy‐makers to design cover crop related government agricultural policies and incentive programs for farmers and agri‐food related industries. [ABSTRACT FROM AUTHOR]

Bio‐inspired fog harvesting devices (FHDs) have been exploited to address water shortages. Despite the advances achieved, efficient and continuous fog harvesting is still hindered by some challenges: 1) a single mechanism appears to be insufficient to achieve optimal water collection efficiency; 2) the re‐evaporation of the harvested water results in decreased water collection. In this study, inspired by the unique feature of cacti, Nepenthes alata, lotus leaves, and pinecones, a 2D–3D switchable copper sheet (SLP‐SHL@SP‐Cu) is reported, which results in excellent fog capture and water transport rates. During the fog harvesting process, the captured droplets transport from the tip of the top surface to the bottom under the Laplace forces (cacti spine), capillary forces (lotus leaves), and low surface energy (Nepenthes alata). Notably, the novel FHD responds in a preprogrammed manner to changes in humidity and performs a dynamic transformation from a 2D planar structure to a 3D helical structure (and in reverse), enabling efficient fog harvesting (in the 3D geometry) and water storage (in its 2D form). Thus, this study opens a new avenue for the practical applications of highly effective FHDs with increased water retention abilities. [ABSTRACT FROM AUTHOR]

Berberine (Ber) is easy to synthesize and has a variety of biological and pharmacological activities. At present, the existing studies on berberine have focused predominantly on its antibacterial activity; its herbicidal activity is rarely reported. In addition, there are a number of preparations of berberine, which are not enough to solve its shortcomings of low solubility and biological activity and the difficult storage of berberine. Here, berberine was combined with carbon dots to obtain carbon dots-berberine (CDs-Ber) nano formulation. The fluorescence quenching results showed that the CDs-Ber nano drug delivery system was successfully constructed, and the fluorescence quenching mechanism of the two was static quenching. The bioassay results showed that CDs had no adverse effects on the growth of barnyard grass (Echinochloa crus-galli) and redroot pigweed (Amaranthus retroflexus), and had high biocompatibility. Berberine and CDs-Ber predominantly affected the root growth of barnyard grass and redroot pigweed and could enhance the growth inhibition effect on weeds, to some extent. The results of the protective enzyme system showed that both berberine and CDs-Ber could increase the activities of Superoxide dismutase (SOD), Peroxidase (POD), and Catalase (CAT) in barnyard grass, and CDs-Ber had a stronger stress effect on barnyard grass than berberine. The determination of the number of bacterial communities in the soil after the berberine and CDs-Ber treatments showed that there was no significant difference in the effects of the two, indicating that CDs-Ber would not have more negative impacts on the environment. The CDs-Ber nano formulation improved the biological activity of berberine, enhanced the herbicidal effect, and was relatively safe for soil colonies. [ABSTRACT FROM AUTHOR]

Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of cover cropping variations and impacts of government policies remains very limited. We developed an accurate and cost‐effective approach utilizing satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most counties was stagnant from 2000 to 2011, but has significantly increased from 2011 to 2021. The adoption of 2021 is four times that of 2011, which was highly correlated to the funding for conservation programs. However, the percentage of cover crop adoption in the U.S. Midwest is still low (7.2%). Our work fills a critical gap in quantifying long‐term field‐level cover crop adoption at large regions and highlights the potential importance of incentive programs to promote sustainable agricultural practices. Plain Language Summary: Cover crops typically grow after cash crop harvesting and before the following season's planting of cash crops, and can bring benefits to agricultural sustainability. To stimulate cover crop adoption in the U.S. Midwest, the U.S. government has made significant financial and technical support to farmers for cover cropping (e.g., USDA Environmental Quality Incentives Program invested over $14 billion in conservation practices from 2010 to 2020). However, large‐scale and long‐term cover cropping information in the U.S. Midwest is largely missing. Timely and cost‐effective monitoring and verification of cover crop adoption are urgently needed. We utilized multi‐source remote sensing data and large amounts of ground truth data to develop effective approaches to detect cover crop adoption from 2000 to 2021 in a cost‐effective manner. We found that cover cropping acreage has significantly increased four times from 2011 to 2021. We also found the increase in cover crop adoption is highly correlated to the funding from federal and state conservation programs. The incentive programs could play an important role in promoting sustainable agricultural practices. Key Points: We developed a scalable remote sensing framework to detect field‐level cover cropping in the U.S. Midwest from 2000 to 2021Cover cropping adoption rate in the U.S. Midwest has significantly increased by four‐fold from 1.8% in 2011% to 7.2% in 2021Cover cropping rate is highly correlated to the incentives of federal and state conservation programs [ABSTRACT FROM AUTHOR]

With the progress of society, sports have become the mainstream of social development. Strengthening the athletic ability of basketball players can effectively improve their shooting percentage. Firstly, virtual reality (VR) technology and motion capture technology are summarized. Secondly, the resistance training and shooting training of basketball players are analyzed and explained. Finally, the algorithm of motion capture technology is designed to capture and optimize the movements of athletes. In addition, a comprehensive evaluation of the shooting percentage of basketball players is carried out. The results show that the motion capture technology proposed here effectively captures the shooting action of basketball players, and the shooting percentage of players is promoted through resistance training. Among all athletes, the highest shooting percentage improvement is around 14% and the lowest is around 4%. In all groups, athletes of different heights have the largest difference in the improvement of shooting percentage. Therefore, this work plays an important role in improving the shooting rate of basketball players through VR technology. It provides technical support for improving the shooting percentage of basketball players and contributes to the progress of athletes' comprehensive athletic ability. [ABSTRACT FROM AUTHOR]

• Non-legume cover crops cause 3.9 % yield penalty for maize, no penalty for soybean. • Legume cover crops have no significant impacts on maize and soybean yield. • Pathways for yield penalty are N and O 2 deficiency, water stress and cooler soil. • Optimizing cover crop growth windows could maximize the benefit from cover crops. • The ecosys model is an effective tool to quantify cover crop impacts on crop yield. Growing cover crops is one of the most promising conservation practices with multiple benefits. However, the impacts of cover crops on the productivity of the maize-soybean [ Zea mays L. - Glycine max (L.) Merr.] rotation system in the U.S. Midwest still have large uncertainties based on results obtained from field experiments, specifically across different soil properties, climate conditions, and land management practices. Process-based models fully validated with data from field experiments across these diverse conditions provide an effective tool to quantify cover crop impacts on cash crop productivity and optimize the management of cover crops accordingly. In this study, we aim to answer the following questions: (1) What are the impacts of cover crops on cash crop yield in the U.S. Midwest agroecosystems? (2) What are the mechanistic pathways of cover crop to affect cash crop yield (e.g. through influencing soil water, nitrogen and O 2 dynamics)? (3) What management practices can be used to mitigate the negative impacts on cash crop yield caused by cover crops? To address these questions, we calibrated and validated a sophisticated process-based agroecosystem model, ecosys , using field experimental data from 2013 to 2018 across Illinois, and then used ecosys to assess the impacts of winter cover crops on maize and soybean yield under different management practices. Our study revealed the following findings: (1) planting non-legume cover crops can cause 3.9 ± 3% yield reduction for maize and no significant impacts on soybean yield, while planting legume cover crops has no significant impact on the yield of either maize or soybean; (2) the maize yield reduction caused by planting cover crops can be mainly explained by nitrogen deficiency induced by increased immobilization, water competition in dry areas, cooler soil surface and oxygen competition; (3) later termination of non-legume cover crops before maize can result in larger maize yield loss due to intensive competition for resources (e.g. water and nitrogen), and the impacts of non-legume cover crops on maize yield reduction can be minimized by optimizing cover crop termination time in the spring. Overall, in the U.S. Midwestern maize-soybean rotation system, we found that although non-legume cover crops cause yield reduction for maize through resource competition, this yield reduction can be minimized through management practices, such as controlling termination time of cover crop and proper fertilizer management. [ABSTRACT FROM AUTHOR]

Improving nitrogen (N) use efficiency is urgently needed to achieve co-sustainability of agricultural productivity and environmental quality. Environmental conditions and farming management practices affect the N cycle in agroecosystems. Particularly, weather conditions during the pre-growing-season (e.g. winter and early spring for the U.S. Corn Belt) can influence the dynamics of soil inorganic N (SIN) content and have implications for the end-of-season crop yield. Here, we used an advanced agroecosystem model, ecosys , to assess the consequences of different pre-growing-season weather scenarios in terms of both SIN dynamics and crop productivity. We first benchmarked ecosys using extensive N trial data collected across the U.S. Midwest, and found that ecosys captured the N fertilizer-yield responses and field-scale N cycle dynamics. We then used ecosys to conduct multiple experiments by changing the pre-growing-season precipitation and temperature, and assessed how these changes affected soil N dynamics and crop yield. We found that: (1) wetter pre-growing-seasons reduced SIN content through increasing leaching, leading to a reduction in corn grain yield of 0.54–0.86 Mg/ha (5–14%) under no fertilizer and of 0.21–0.33 Mg/ha (1–3%) under the normal N fertilizer rate (167 kg N/ha; Illinois average N fertilizer rate in 2018); yield loss induced by higher pre-growing-season precipitation can be eliminated by applying more N fertilizer in spring; and (2) colder pre-growing-seasons can reduce SIN content through decreased N mineralization and enhanced leaching. Both factors further contribute to corn yield loss of 0.10–0.68 Mg/ha (2–8%) under no fertilizer and of 0.12–0.48 Mg/ha (1–4%) under the normal fertilizer rate; however, in this case adding more fertilizer does not necessarily eliminate the yield loss caused by the colder pre-growing-season, because the lower temperature not only causes SIN deficiency but also reduces early-growing-season active root nutrients uptake and crop N demand by cooling soil temperature. These findings expand our understanding of the impact of weather conditions on crop yield and can inform improvements in N fertilizer use efficiency in the U.S. Midwest agroecosystems. [ABSTRACT FROM AUTHOR]

Atmospheric water harvesting (AWH) has been broadly exploited to meet the challenge of water shortage. Despite the significant achievements of AWH, the leakage of hydroscopic salt during the AWH process hinders its practical applications. Herein, inspired by the unique selective permeability of the phospholipid bilayer, a sandwich structural (hydrophobic-hydrophilic-hydrophobic) polyacrylonitrile nanofibrous membrane (San-PAN) was fabricated for AWH. The hydrophilic inner layer loaded with LiCl could capture water from the air. The hydrophobic microchannels in the outer layer could selectively allow the free transmission of gaseous water molecules but confine the hydroscopic salt solution in the hydrophilic layer, achieving continuous and recyclable water sorption/desorption. As demonstrated, the as-prepared AWH devices presented high-efficient adsorption kinetics from 1.66 to 4.08 g g -1 at 30% to 90% relative humidity. Thus, this work strengthens the understanding of the water transmission process along microchannels and provides insight into the practical applications of AWH.

Insulin resistance (IR) is one of the pathological reasons for type II diabetes mellitus (T2DM). Therefore, it is important to prevent the body from developing T2DM by improving IR and maintaining glucose homeostasis. Anthocyanins (ACNs) are water-soluble pigments and are widely distributed in natural products. This article summarizes research on the bioavailability and metabolism of ACNs. Moreover, we further elaborate on how ACNs reduce IR and hyperglycemia during the development of T2DM based on studies over the past 20 years. Many studies have demonstrated that ACNs are small molecules that target the pancreatic, liver, muscle, and adipose tissues, preventing IR and hyperglycemia. However, the molecular mechanisms are still unclear. Therefore, we envision whether the molecular mechanism of reducing T2DM by ACNs could be more deeply investigated.