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ABSTRACT Switchgrass (Panicum virgatumL.) is a native North American grass currently considered a high‐potential bioenergy feedstock crop. However, previous reports questioned its effectiveness in generating soil organic carbon (SOC) gains, with resultant uncertainty regarding the monoculture switchgrass's impact on the environmental sustainability of bioenergy agriculture. We hypothesize that the inconsistencies in past SOC accrual results might be due, in part, to differences in prior land management among the systems subsequently planted to switchgrass. To test this hypothesis, we measured SOC and other soil properties, root biomass, and switchgrass growth in an experimental site with a 30‐year history of contrasting tillage and N‐fertilization treatments, 7 years after switchgrass establishment. We determined switchgrass' monthly gross primary production (GPP) for six consecutive years and conducted deep soil sampling. Nitrogen fertilization expectedly stimulated switchgrass growth; however, a tendency for better plant growth was also observed under unfertilized settings in the former no‐till soil. In topsoil, SOC significantly increased from 2007 to 2023 in fertilized treatments of both tillage histories, with the greatest increase observed in fertilized no‐till. Fertilized no‐till also had the highest particulate organic matter content in the topsoil, with no differences among the treatments observed in deeper soil layers. However, regardless of fertilization, the tillage history had a strong effect on stratification with depth of SOC, total N, and microbial biomass C. Results suggested that historic and ongoing N fertilization had a substantial impact on switchgrass growth and soil characteristics, while tillage legacy had a much weaker, but still discernible, effect. 

Widespread expansion of agriculture and forestry has altered the surface of the Earth, the composition of the atmosphere, and as a result, the climate. Here we quantify the radiative forcing caused by the deforestation of an ecoregion of the U.S. Upper Midwest and the adoption of eight nature-based climate solutions. We combined forest inventory data with over three decades of remote sensing and in situ data from a replicated land use change experiment. Deforestation of the region caused net global warming (1626 ± 44 µW m-2), mainly from the 76 % reduction of ecosystem carbon stocks, but also from the 84 % reduction of the soil methane sink and the 115 % increase in soil nitrous oxide emissions. The associated albedo increase offset 24 % of the greenhouse gas induced warming. For the adoption of nature-based climate solutions, we found that conservation agriculture provided a modest -39 to -76 ± 31 µW m-2 of climate mitigation, short/medium length forestry rotations provided more at -296 to -881 ± 44 µW m-2, and natural forest regeneration provided the most at -1555 ± 44 µW m-2. As the impacts of climate change on nature and society intensify, consideration should be given to the climate mitigation, habitat, and ecosystem services that nature-based climate solutions can provide. 

Changes in soil organic carbon (SOC) and nitrogen (SON) are strongly affected by land management, but few long-term comparative studies have surveyed changes throughout the whole soil profile. We quantified 25-year SOC and SON changes to 1 m in 10 replicate ecosystems at an Upper Midwest, USA site. We compared four annual cropping systems in maize (Zea mays)-soybean (Glycine max)-winter wheat (Triticum aestivum) rotations, each managed differently (Conventional, No-till, Reduced input, and Biologically based); in three managed perennial systems (hybrid Poplar (Populus × euramericana), Alfalfa (Medicago sativa), and Conifer (Pinus spp.); and in three successional systems (Early, Mid- and Late succession undergoing a gradual change in species composition and structure over time). Both Reduced input and Biologically based systems included winter cover crops. Neither SOC nor SON changed significantly in the Conventional or Late successional systems over 25 years. All other systems gained SOC and SON to different degrees. SOC accrual was fastest in the Early successional system (0.8 ± 0.1 Mg C ha−1 y−1) followed by Alfalfa and Conifer (avg. 0.7 ± 0.1 Mg C ha−1 y−1), Poplar, Reduced input, and Biologically based systems (avg. 0.4 ± 0.1 Mg C ha−1 y−1), and Mid-successional and No-till systems (0.3 and 0.2 Mg C ha−1 y−1, respectively). Over the most recent 12 years, rates of SOC accrual slowed in all systems except Reduced input and Mid-successional. There was no evidence of SOC loss at depth in any system, including No-till. Rates of SON accrual ranged from 64.7 to 0.8 kg N ha−1 y−1 in the order Alfalfa ≥ Early successional > Reduced input and Biologically based ≥ Poplar > No-till and Conifer > Mid-successional systems. Pyrogenic C levels in the Conventional, Early, and Late successional systems were similar despite 17 years of annual burning in the Early successional system (∼ 15 % of SOC to 50 cm, on average, and ∼40 % of SOC from 50 to 100 cm). Results underscore the importance of cover crops, perennial crops, and no-till options for sequestering whole profile C in intensively managed croplands. 

Abstract Agricultural researchers are increasingly encouraged to engage with stakeholders to improve the usefulness of their projects, but iterative research on the design and assessment of stakeholder engagement is scarce. The USDA Long‐Term Agroecosystem Research (LTAR) Network recognizes the importance of effective engagement in increasing the utility of information and technologies for future agriculture. Diverse stakeholders and researchers at the Kellogg Biological Station (KBS) LTAR site co‐designed the KBS LTAR Aspirational Cropping System Experiment, a process that provides a testing ground and interdisciplinary collaborations to develop theory‐driven assessment protocols for continuous stakeholder engagement. Informed by prior work, we designed an assessment protocol that aims to measure participant preferences, experiences, and perceived benefits at various stages of this long‐term project. Two online surveys were conducted in 2021 and 2022 among participants of LTAR engagement events at KBS, using a pre‐post design, resulting in 125 total responses. Survey respondents had positive perceptions of the collaboratively designed research experiment. They had a strong expectation that the research would generate conservation and environmental advances while also informing policy and programs. Respondents also indicated a desire to network with other stakeholders. The research team noted the significant role of a long‐term stakeholder engagement specialist in inviting participants from diverse backgrounds and creating an open and engaging experience. Overall, results highlight an interdisciplinary path of intentional and iterative engagement and evaluation to build a program that is adaptive and responsive to stakeholder needs. 

Associative N2 fixation (ANF) is widespread but poorly characterized, limiting our ability to estimate global inputs from N2 fixation. In some places, ANF rates are at or below detection most of the time, but occasionally and unpredictably spiking to very high rates. Here we test the hypothesis that plant phenology and rainfall events stimulate ANF episodes. We measured ANF in intact soil cores in switchgrass (Panicum virgatum L.) in Michigan, USA. We used rain exclusion shelters to impose three rainfall treatments with each receiving 60 mm of water over a 20-day period but at different frequencies. We concurrently established a treatment that received ambient rainfall, and all four treatments were replicated four times. To assess the effects of plant phenology, we measured ANF at key phenological stages in the ambient treatment. To assess the effects of rainfall, we measured ANF immediately before and immediately after each wetting event in each treatment involving rainfall manipulation. We found that the previous day’s rainfall could explain 29% of the variation in ANF rates within the ambient treatment alone, and that bulk soil C:N ratio was also positively correlated with ANF, explaining 18% of the variation alone. Wetting events increased ANF and the magnitude of response to wetting increased with the amount of water added and decreased with the amount of inorganic N added in water. ANF episodes thus appear to be driven primarily by wetting events. Wetting events likely increase C availability, promote microbial growth, and make rhizosphere conditions conducive to ANF. 

Abstract Soil nitrous oxide (N₂O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003–2019) of measurements at the Kellogg Biological Station Long‐Term Ecological Research (LTER)/Long‐Term Agroecosystem Research (LTAR) site to better understand the controls of N₂O emissions in four corn–soybean–winter wheat rotations employing conventional, no‐till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N₂O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2‐day precipitation, water‐filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%–42% of daily N₂O flux variability in the test data, with greater predictability for the corn phase in each system. The long‐term rotations showed different controlling factors and threshold conditions influencing N₂O emissions. In the conventional system, the model identified ammonium (>15 kg N ha⁻¹) and daily air temperature (>23°C) as the most influential variables; in the no‐till system, climate variables such as precipitation and air temperature were important variables. In low‐input and organic systems, where red clover (Trifolium repensL.; before corn) and cereal rye (Secale cerealeL.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N₂O emissions, followed by precipitation and air temperature. In low‐input and biologically based systems, red clover residues increased soil nitrogen availability to influence N₂O emissions. Long‐term data facilitated machine learning for predicting N₂O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.