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Soil fertility management for sustainable Miscanthus × giganteus production: Increased tiller weight from nitrogen management explains yield gains in aged miscanthus
Aging-related yield decline in Miscanthus × giganteus (miscanthus) remains a major constraint to sustainable biomass production. This study evaluated how nitrogen (N) management and soil fertility influence yield-component traits and productivity in aging miscanthus. Trials were conducted at two sites established in 2008 at the University of Illinois Energy Farm, Urbana, IL. (i) The Sun Grant trial received 0, 60, and 120 kg N ha −1 annually until 2015. Starting 2021, half of each plot received 60 or 120 kg N ha −1 , resulting in six legacy-contemporary treatments: 0N–0N, 0N–120N, 60N–0N, 60N–60N, 120N–0N, 120N–120N. (ii) The Energy Farm trial remained unfertilized until 2014, when one half of each plot received 56 kg N ha −1 , forming two treatments: 0N–0N, 0N–56N. Sun Grant trial results showed N fertilization increased tiller density (tillers m −2 ) and tiller weight (g tiller −1 ) in juvenile to early-mature miscanthus (2011–2015). After N withdrawal, both traits declined (20 % and 40 %), though legacy effects persisted in tiller weight in the aging stands (2020–2023). Contemporary N had little effect on tiller density but increased tiller weight by 34 %–77 %, resulting in 23 %–106 % higher machine-harvested biomass yield in 0–120N, 60-60N, and 120-120N plots. At the Energy Farm trial, 0N–56N plots yielded 59 %–108 % more biomass than 0N–0N. Soil total N increased (Sun Grant: 47 % by 2020; Energy Farm: 58 % by 2023), while Mehlich-3 P (42 %–44 %) and K (21 %–46 %) declined. These findings identify tiller weight as a key determinant of biomass yield in aging miscanthus and highlight the need for P and K management for long-term productivity.
Macromolecular rate theory explains the temperature dependence of membrane conductance kinetics
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Elucidating precipitation in FeCrAl alloys through explainable AI: A case study
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Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks
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Dietary microalgae on poultry meat and eggs: explained versus unexplained effects
Different types and sources of microalgae are used to feed broiler chickens and laying hens. This report provides a concise update on various impacts of feeding these novel ingredients on physical, chemical, and nutritional attributes of the resultant meat and eggs. Some of the observed effects may be associated with biochemical and molecular mechanisms derived from unique chemical compositions and nutritional values of microalgae. However, the full potential and the accurate mechanism of microalgae in producing health-promoting poultry foods remain to be explored.
Explainable rule-based prediction of cultivation media for microbes
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Streamflow duration curve to explain nutrient export in Midwestern USA watersheds: Implication for water quality achievements
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Structural and Dynamic Features of Acinetobacter baumannii OXA-66 β-Lactamase Explain Its Stability and Evolution of Novel Variants
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Explainability and human intervention in autonomous scanning probe microscopy
The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.
Can a protophobic vector boson explain the ATOMKI anomaly?
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A Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products
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Structure and Dynamics of Water in Polysaccharide (Alginate) Solutions and Gels Explained by the Core–Shell Model
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Evaluation of Nanaerobic Digestion as a Mechanism to Explain Surplus Methane Production in Animal Rumina and Engineered Digesters
Nanaerobes are a newly described class of microorganisms that use a unique cytochrome bd oxidase to achieve nanaerobic respiration at <2 μM dissolved oxygen (~1% of atmospheric oxygen) but are not viable above this value due to the lack of other terminal oxidases. Although sharing an overlapping ecological niche with methanogenic archaea, the role of nanaerobes in methanogenic systems has not been studied so far. To explore their occurrence and significance, we re-analyzed published meta-omic datasets from animal rumina and waste-to-energy digesters, including conventional anaerobic digesters and anaerobic digesters with ultra-low oxygenation. Results show that animal rumina share broad similarities in the microbial community and system performance with oxygenated digesters, rather than with conventional anaerobic digesters, implying that trace levels of oxygen drive the efficient digestion in ruminants. The rumen system serves as an ideal model for the newly named nanaerobic digestion, as it relies on the synergistic co-occurrence of nanaerobes and methanogens for methane yield enhancement. The most abundant ruminal bacterial family Prevotellaceae contains many nanaerobes, which perform not only anaerobic fermentation but also nanaerobic respiration using cytochrome bd oxidase. These nanaerobes generally accompany hydrogenotrophic methanogens to constitute a thermodynamically and physiologically consistent framework for efficient methane generation. Our findings provide new insights into ruminal methane emissions and strategies to enhance methane generation from biomass.