Abstract Agriculture depletes soil organic carbon (SOC), partly due to the exclusion of deep‐rooted perennials. Reintroducing deep‐rooted perennials to cultivated land may help to mitigate SOC loss. We quantified the effect of deep roots on SOC by comparing 8 to 30 year‐old stands of switchgrass (Panicum virgatum L.) with paired annual row crop fields at 12 sites across the central and eastern USA. We hypothesized that switchgrass would store more root C and SOC than neighboring shallow‐rooted annual crops, and that these effects would extend deeper than 30 cm. We also evaluated whether switchgrass stimulates decomposition of SOC at depth using radiocarbon (14C). Finally, we explored whether the effect of switchgrass on SOC is moderated by soil chemical and physical properties. While the effect of switchgrass on SOC in the surface 100 cm was positive at most sites, the average effect was not statistically significant (difference in SOC = 0.6 kg C m−2 [95% CI −0.8 to +1.9 kg C m−2]). By contrast, we found that root C was consistently more abundant under switchgrass, yielding an estimated additional 0.6 kg C m−2 in the surface 100 cm of soil [95% CI +0.5 to +0.7 kg C m−2]. 14C measurements suggested that root C inputs were adding to existing SOC without stimulating decomposition. The effect of switchgrass on belowground C was not strongly related to any of the soil properties that we evaluated. Our observations show that root C can contribute substantially to belowground C stocks when deep‐rooted perennials replace shallow‐rooted crops.
Sensemaking plays a critical role in social media, which features ambiguous, equivocal, and dynamic information, views, and opinions. While existing research has focused on sensemaking with respect to text entries related to extreme events, we examine sensemaking with respect to video content on social media. Drawing on sensemaking theories, we investigate the relationships between video features and sensemaking activities. Specifically, we analyze how information control, cue, and noise in YouTube videos affect sensemaking activities on YouTube and Reddit. Our findings reveal both similarities and differences in how these factors relate to sensemaking activities across the two platforms. This research enriches the current literature on social media sensemaking and provides insights for designing videos that better facilitate sensemaking activities and enhance user experiences.
Enhancing the production of high-value metabolites in microalgae is essential for sustainable bioproducts and biofuels. In this study, Porphyridium purpureum cultures were supplemented with β-cyclodextrin (β-CD) and succinic acid (SA) to evaluate their effects on biomass, lipid accumulation, fatty acid profiles, phycobiliproteins, and exopolysaccharides. Moderate β-CD concentrations (0.25–0.50 g L− 1) significantly increased biomass (2.98 ± 0.12 g L− 1), lipid content (277.00 ± 5.56 mg g− 1), total fatty acids, and arachidonic acid yield (70.60 ± 5.70 mg L− 1) compared to the control. SA supplementation (0.30–0.60 g L− 1) selectively enhanced long-chain polyunsaturated fatty acids accumulation compared with the control treatment, with arachidonic acid reaching 45.89 ± 5.70 mg L− 1. Both supplements also elevated phycobiliproteins and exopolysaccharides, with phycoerythrin being the dominant phycobiliprotein, reaching 146.88 ± 12.40 mg L− 1 under 0.50 g L− 1 β-CD and 147.00 ± 7.50 mg L− 1 under 0.60 g L− 1 SA. Excessive levels of either supplement, however, negatively affected growth and metabolite productivity. These findings demonstrate that controlled β-CD and SA supplementation can efficiently improve biomass and the accumulation of lipids, long-chain PUFAs, PBPs, and EPS in P. purpureum, offering a practical strategy for industrial-scale production of high-value algal compounds.
Accurate drought forecasting is essential for effective water resource management, agricultural planning, and disaster risk reduction, particularly in the context of climate change. Nepal is significantly impacted by drought, yet forecasting efforts in this region remain underexplored. This study aims to analyze drought trends using the Standardized Precipitation Evapotranspiration Index (SPEI) at five timescales (SPEI-1, SPEI-3, SPEI-6, SPEI-9, and SPEI-12) and to forecast these indices using machine learning techniques. Meteorological data from 59 stations across Nepal were used to calculate SPEI values, with data from 1980 to 2013 used for training the LSTM model and data from 2014 to 2023 for testing. Meteorological stations were grouped into seven homogeneous climatic regions using K-means clustering. We employed a long short-term memory (LSTM) model, well-suited for capturing temporal patterns, to predict drought conditions. The model’s performance was evaluated using metrics such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Our findings reveal a significant decreasing trend (approximately 50
Abstract Wildfire emission inventories exhibit large variability that complicates assessments of smoke impacts. Here we compare fuel consumed (in mass per burned area units) from multiple burn area‐based and energy‐based approaches for fires in the western US during 2020. Average fuel consumed can vary by up to factors of 2–16 between approaches across burn severity classes and fuel types. Fuel consumed estimates typically increase with burn severity, except for the energy‐based approaches for forest land cover, where it decreases for high burn severity. Also, in contrast to other approaches, energy‐based estimates decrease for tree cover greater than 40% regardless of burn severity class. This implies that corrections to the energy‐based approach are likely needed across burn severity categories to account for canopy and smoke shading. The methodological recommendations provided would likely result in greater consistency between wildfire emission estimates and highlight the need to better constrain fuel loading and consumption.
