Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification frameworks of carbon markets, but model-specific differences limit their applicability across diverse pedo-climatic conditions and agronomic practices. Multi-model ensemble (MME) provides an opportunity to better estimate changes in soil organic carbon (SOC) and nitrous oxide (N2O) emissions from agronomic practices at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4-km2 to assess the aggregate ability of different regenerative practices to sequester SOC and N2O emissions compared to their counterfactual dynamic baselines. MME was validated against long-term trials and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 ± 0.12 Mg ha-1 yr-1, corresponding to a net regional SOC gain of 16.4 Tg C yr-1 compared to business-as-usual baselines. These benefits are halved when each management is practiced individually, and the SOC gains are only fully realized with low initial carbon stock. By including N₂O emissions, we can assess the overall climate mitigation potential, specifically, the extent to which carbon sequestration can offset direct N2O emissions. The magnitude of this potential varies depending on management practices and geographic location with net climate benefits on average ranging from 0 to 3 Mg CO2-eq ha-1 yr-1. High-resolution MME results allow for robust estimates of climate mitigation, reducing barriers to carbon market participation and supporting regenerative agriculture initiatives at scale.

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification (MMRV) frameworks of carbon markets, but their application suffers from model-specific differences that keep any one model from working well across all combinations of soils, climates, crops, and agronomic practices at varying scales. Multi-model ensemble (MME), successfully used to quantify soil, management and climate impact on crop productivity, provide an opportunity to better estimate changes in soil organic carbon (SOC) outcomes for agronomic practices that have the potential to mitigate SOC loss at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4-km2 to assess the aggregate ability of different regenerative practices to sequester SOC at this scale compared to their dynamic baselines. MME was validated with long-term experimental data and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 +/- 0.12 Mg ha-1 yr-1 aggregated across the entire U.S. Midwest cropland. At the regional scale, this corresponds to a net SOC gain of 16.4 Tg C yr-1 compared to business-as-usual baselines. These benefits are approximately halved when each management change is practiced individually, and the modest gains are only fully realized when continued over the long-term in soils with low initial carbon stock. Results demonstrate the power of MMEs run at high resolution for providing robust estimates of environmental outcomes following agricultural practice change, and for pinpointing locations for most effective intervention. This approach can alleviate many producer carbon market participation barriers and help address market issues while ultimately supporting large-scale regenerative agriculture initiatives. ### Competing Interest Statement Bruno Basso is a cofounder of CIBO Technologies. Keith Paustian and Yao Zhang have financial interest in Indigo Ag. The other coauthors declare no conflict of interest.

Increasing resource demands and efforts to mitigate anthropogenic impacts have thrust circular bioeconomy into the spotlight. However, there currently lacks a metric to provide singular quantification of circularity. This study showed development of a Circularity Index (CI) with value between 0 (completely linear) and 1 (completely circular) to quantify circularity of resource flows at different system scales, identify weak links in value-chains, and determine tradeoffs in the system. This study describes 1) CI for systems containing consumable, renewable, and recovered resources; 2) CI application for two examples: nitrogen in a corn-soybean farm and energy in the U.S. food and agricultural system. CI showed that nitrogen circularity increased from 0.687 to 0.860 through implementation of renewable fertilizer from manure compared to synthetic fertilizer. CI also demonstrated improved energy circularity in the U.S. food and agricultural system, increasing from 0.179 to 0.843 when integrating food-energy-water systems via hydrothermal liquefaction and nutrient recycling.

Highlights ASABE has created a society initiative on transforming food and agricultural systems to achieve greater circularity. A task force has been charged by ASABE with guiding the initiative effort. Transforming to more circular bioeconomy systems will require multiple disciplines, policy makers, and inclusion of economics, societal, and environmental aspects. The special collection topics include conversion of wastes into usable products, incorporation of sustainability objectives into production systems and supply chains, assessment of a system’s circularity, and workforce education for achieving circularity in agricultural and food systems. Abstract. The American Society of Agricultural and Biological Engineers (ASABE) launched a new initiative in 2020 – Transforming Food and Agriculture to Circular Systems (TFACS) – that calls for system-level solutions. Linear systems focus on creating a profitable yield while considering the financial costs of inputs (e.g., water, nutrients, energy) with little regard to resource use efficiencies and the broader impacts of losses and wastes. Circular systems consider a more holistic view for transforming systems using principles that: (1) design out waste and pollution; (2) keep products and materials in use, (reuse, share, repair, refurbish, remanufacture, recycle); (3) regenerate natural systems; (4) increase the productivity of resource use; and (5) provide economic benefits. This introduction to the Special Collection of articles provides an overview of ASABE’s initiative to accelerate the growth of circular bioeconomy systems and introduces the articles in this ASABE Special Collection. Articles in this collection provide examples showing the benefits of combining circular economy and bioeconomy concepts to develop the cascading use of biomass from biological resources for economic development. The articles also identify the need for additional work to move society toward circular food and agricultural systems. Efforts to rethink and redesign future systems using circular bioeconomy concepts can rapidly create more sustainable, resilient, and equitable food and agricultural systems. Keywords: Bioeconomy, Circular economy, Circularity, Convergence, Food and agricultural systems, Systems thinking, Systems of systems.

BackgroundPredicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. ResultsWe describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean ( Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype , Environment , and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model.ConclusionsThe dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

This chapter evaluates the potential for double cropping soybean in North Florida and to determine combinations of cultivars to plant for maximum yield. A soybean phenology model for Version 5.0, with cultivar specific parameters that depend on night length and temperature, has been developed. Component models of the soybean production system (crop, soil, insect, disease, management, and economic) formed the basis for several specialized application models. Biomass growth of the crop is based on a carbohydrate balance, which includes photosynthesis, respiration, partitioning, remobilization of protein, and senescence. Three levels of studies (Boggess, Swaney, and Boggess and Amerling) have been performed using SOYGRO V4.2 for analyzing the economics of soybean irrigation management in Florida. Irrigation of soybeans in rotation with maize and peanuts also would be profitable with low risks. In order to predict soybean growth and yield, one must be able to predict accurately the timing and duration of various crop growth phases.

HighlightsWe describe and demonstrate a multidimensional framework to integrate environmental and genomic predictors to enable crop improvement for a circular bioeconomy.A model training procedure based on multiple phenotypes is shown to improve predictive skill.The decision set comprised of model outputs can inform selection for both productivity and circularity metrics.Abstract. Contemporary agricultural systems are poised to transition from linear to circular, adopting concepts of recycling, repurposing, and regeneration. This transition will require changing crop improvement objectives to consider the entire system, and thus provide solutions to improve complex systems for higher productivity, resource use efficiency, and environmental quality. The methods and approaches that underpinned the doubling of yields during the last century may no longer be fully adequate to target crop improvement for circular agricultural systems. Here we propose a multidimensional framework for prediction with outcomes useful to assess both crop performance traits and environmental sustainability of the designed agricultural systems. The study focuses on maize harvestable grain yield and total carbon production, water use, and use efficiency for yield and carbon. The framework builds on the crop growth model whole genome prediction system, which is enabled by advanced phenomics and the integration of symbolic and sub-symbolic artificial intelligence. We demonstrate the approach and prediction accuracy advantages over a standard statistical genomic prediction approach used to breed maize hybrids for yield, flowering time, and kernel set using a dataset comprised of 7004 hybrids, 103 breeding populations, and 62 environments resulting from six years of experimentation in maize drought breeding in the U.S. We propose this framework to motivate a dialogue for how to enable circularity in agriculture through prediction-based systems design. Keywords: Circular bioeconomy, Circular economy, Crop improvement, Crop models, Drought, Gene editing, Genomic prediction, Maize, Plant breeding.

Recent studies have shown a good relationship of soybean seed yield with canopy CO2 assimilation, especially if measured during seed filling. Assimilation of irrigated plants responded predictably to the diurnal cycle in photosynthetic photon flux density (PPFD) with no mid-day depression. A strong temperature effect on concurrent crop respiration may play a role in the diurnal hysteresis loop of apparent canopy photosynthesis (ACP) or net canopy photosynthesis plotted versus PPFD. Much of the decline in canopy assimilation during seed filling is associated with N remobilization from leaf tissue. Larson et al. measured mid-day ACP for a range of cultivars and found the maximum relative difference to be 16%. Photosynthetic response to insect defoliation is proposed to act primarily via reductions in leaf area index (LAI). Although the data were not covariance adjusted for concurrent decline in seasonal PPFD or LAI, the seasonal decline in total canopy photo-synthesis was much better associated with N concentration than with LAI.

Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as “genetic coefficients.” These GSPs are estimated using phenotypic observations and may not represent “true” genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study conducted in 2011 and 2012 included 187 recombinant inbred lines (RILs) from a bi-parental bean family to measure the effects of quantitative trait loci (QTL), environment (E), and QTL×E interactions across five sites. The dynamic mixed linear model from Vallejos et al. (2020) was modified in this study to create a dynamic module that was then integrated into the CSM-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic makeup of each genotype being simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and G×E effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.

Introduction: There is an urgent need to transform unsustainable "linear" grain production systems in the United States (U.S.) and other countries like China, Brazil, Argentina, Canada, Russia, Australia and Europe, into more circular and sustainable systems to address the simultaneous challenges of resource depletion, environmental degradation, and the growing global demand for food under the threat of climate change. Objectives: In this perspective, we survey the current state of circularity of U.S. grain production, and discuss how we can transform the systems into more circular systems. Results: Specifically, we lay out a vision of circular grain production enabled by novel digital, mechanical, and biological technologies that allow closing loops of nutrient and energy flows within the farm, through the optimization of land-use choices and crop management. We also examine market-and policy-based mechanisms that could incentivize the widespread adoption of these key technologies.

In silico plant modelling is the use of dynamic crop simulation models to evaluate hypothetical plant traits (phenology, processes and plant architecture) that will enhance crop growth and yield for a defined target environment and crop management (weather, soils, limited resource). To be useful for genetic improvement, crop models must realistically simulate the principles of crop physiology responses to the environment and the principles by which genetic variation affects the dynamic crop carbon, water and nutrient processes. Ideally, crop models should have sufficient physiological detail of processes to incorporate the genetic effects on these processes to allow for robust simulations of response outcomes in different environments. Yield, biomass, harvest index, flowering date and maturity are emergent outcomes of many interacting genes and processes rather than being primary traits directly driven by singular genetics. Examples will be given for several grain legumes, using the CSM-CROPGRO model, to illustrate emergent outcomes simulated as a result of single and multiple combinations of genotype-specific parameters and to illustrate genotype by environment interactions that may occur in different target environments. Specific genetically influenced traits can result in G × E interactions on crop growth and yield outcomes as affected by available water, CO2 concentration, temperature, and other factors. An emergent outcome from a given genetic trait may increase yield in one environment but have little or negative effect in another environment. Much work is needed to link genetic effects to the physiological processes for in silico modelling applications, especially for plant breeding under future climate change.

Unique capabilities of various systems for studying the impacts of rising atmospheric CO2 concentration and other environmental factors on growth and yield of plants are presented. These systems include soil-plant-atmosphere research (SPAR) chambers, free-air carbon dioxide enrichment (FACE) facilities, temperature-gradient greenhouses (TGG), and open top chambers (OTC). The SPAR chambers have several advantages compared to FACE and other facilities, including: (a) constant CO2 concentration and stabile setpoints; (b) CO2 concentration controlled to any range of sub-ambient through supra-ambient levels, providing comparison of plant responses to past and future climates; (c) precise air and dewpoint temperature setpoints; (d) calculation of whole-canopy photosynthesis and evapotranspiration rates at short time intervals; (e) calculation of whole-canopy respiration rates during the night; (f) determination of plant responses to temperature alone or including other factors; (g) multiple chambers for simultaneous comparison of plant responses to varying environments, providing data for plant growth modeling; (h) low operating expense for CO2 concentration; and (i) capability of measuring N-2 fixation rates in the rooting zone of legumes or methane emissions from rice (Oryza sativa L.). SPAR systems were better suited than FACE systems for more than half of the attributes of enrichment systems identified in this paper. Limitations of plant responses due to fluctuating elevated CO2 concentration and limitations of range of elevated CO2 concentration exist for FACE systems. Controlled environments are needed for developing mathematical growth response functions under a wide range of conditions. Finally, we identified a use of portable SPAR chambers within FACE experiments for confirmation of diminished plant photosynthesis in fluctuating CO2 concentration.

PURPOSE: Undergraduate exercise science students can benefit from curriculum which includes authentic, hands-on opportunities for learning. A 12-lead electrocardiograph (ECG) can serve as both as a teaching and screening tool to assess cardiac abnormalities in seniors (over age 65) prior to beginning an exercise program. The purpose of this pilot study was to evaluate the ECG characteristics of older adults prior to participation in a twice-weekly supervised strength training program. METHODS: Thirty seniors (Males = 10; Females = 20; Age =72 ± 7.6yrs) completed cardiovascular screening with resting 12-lead ECG analysis prior to program participation. An exercise physiologist reviewed all ECG results and any identified abnormalities were referred to a cardiologist. Gender, ECG abnormalities, and anthropometrics were compared using a mixed model ANOVA. Chi-square analysis was used to test for differences in the frequency of ECG findings across gender. RESULTS: Thirty seniors (Males = 10; Females = 20; Age =72 ± 7.6yrs) completed cardiovascular screening with resting 12-lead ECG analysis prior to program participation. An exercise physiologist reviewed all ECG results and any identified abnormalities were referred to a cardiologist. Gender, ECG abnormalities, and anthropometrics were compared using a mixed model ANOVA. Chi-square analysis was used to test for differences in the frequency of ECG findings across gender CONCLUSIONS: A pre-exercise ECG can be a useful teaching and screening tool for students who are preparing to supervise older adults in a structured strength training program. ECG results can be used to adjust training variables (type, duration, and intensity) accordingly for each individual senior participant.

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

A simple description of a circular economy is an economic system that uses resources sparingly and recycles materials endlessly so that resources are not trapped in landfills and create environmental problems. Three principles for circular economies have been proposed: 1. Design out waste and pollution. 2. Keep products and materials in use. 3. Regenerate natural systems.

Author(s): Rice, Charles W; Schoen, Robin; Aristidou, Aristos; Burgess, Shane C; Capalbo, Susan; Czarnecki-Maulden, Gail; Dunham, Bernadette; Ejeta, Gibesa; Famigilietti, Jay S; Gould, Fred; Hamer, John; Jackson-Smith, Douglas B; Jones, James W; Kebreab, Ermias; Kelley, Stephen S; Leach, Jan E; Lougee, Robin; McCluskey, Jill J; Plaut, Karen I; Salvador, Ricardo J; Sample, V Alaric