Miscanthus x giganteus (miscanthus), a perennial biomass crop, allocates more carbon belowground and typically has lower soil greenhouse gas (GHG) emissions than conventional feedstock crops, but best practices for nutrient management that maximize yield while minimizing soil GHG emissions are still debated. This study evaluated the effects of four different fertilization treatments (digestate from a biodigester, synthetic fertilizer (urea), hydrochar from the hydrothermal carbonization of digestate, and a control) on soil GHG emissions and biomass yield of an established miscanthus stand grown on abandoned agricultural land. Soil GHG fluxes (including CH4, CO2, and N2O) were sampled in all treatments using the static chamber methodology. Average biomass yield varied from 20.2 Mg ha−1 to 23.5 Mg ha−1, but there were no significant differences among the four treatments (p > 0.05). The hydrochar treatment reduced mean CO2 emissions by 34% compared to the control treatment, but this difference was only statistically significant in one of the two sites tested. Applying digestate to miscanthus resulted in a CH4 efflux from the soil in one of two sites, while soils treated with urea and hydrochar acted as CH4 sinks in both sites. Overall, fertilization did not significantly improve biomass yield, but hydrochar as a soil amendment has potential for reducing soil GHG fluxes.

Soil hydraulic properties are important for the movement and distribution of water in agricultural soils. The ability of plants to easily extract water from soil can be limited by the texture and structure of the soil, and types of soil amendments applied to the soil. Superabsorbent polymers (hydrogels) have been researched as potential soil amendments that could help improve soil hydraulic properties and make water more available to crops, especially in their critical growing stages. However, a lack of a comprehensive literature review on the impacts of hydrogels on soil hydraulic properties makes it difficult to recommend specific types of hydrogels that positively impact soil hydraulic properties. In addition, findings from previous research suggest contrasting effects of hydrogels on soil hydraulic properties. This review surveys the published literature from 2000 to 2020 and: (i) synthesizes the impacts of bio-based and synthetic hydrogels on soil hydraulic properties (i.e., water retention, soil hydraulic conductivity, soil water infiltration, and evaporation); (ii) critically discusses the link between the source of the bio-based and synthetic hydrogels and their impacts as soil amendments; and (iii) identifies potential research directions. Both synthetic and bio-based hydrogels increased water retention in soil compared to unamended soil with decreasing soil water pressure head. The application of bio-based and synthetic hydrogels both decreased saturated hydraulic conductivity, reduced infiltration, and decreased soil evaporation. Hybrid hydrogels (i.e., a blend of bio-based and synthetic backbone materials) may be needed to prolong the benefit of repeated water absorption in soil for the duration of the crop growing season.

Superabsorbent polymers (SAPs), sometimes known as hydrogels, have been proposed as soil amendments to enhance soil water management. But the performance of SAPs as soil amendments depends on their stability in soil. Bio‐based SAPs have been praised as environmentally sustainable due to their apparent fast biodegradation relative to synthetic SAPs. But the fast biodegradation of bio‐based SAPs may come at a cost to their long‐term performance for repeated absorption and release of water in the soil. The purpose of this review is to (i) concisely summarize the methods and mechanisms involved in the biodegradation of different bio‐based and synthetic SAPs, (ii) critically review studies conducted on the biodegradability of bio‐based and synthetic SAPs when used as soil amendments, and (iii) discuss the implications of the biodegradability of bio‐based and synthetic SAPs on their physical properties and stability in soil and (iv) identify potential research directions. Understanding the biodegradability of synthetic compared to bio‐based SAPs and their advantages and disadvantages as soil amendments is important to researchers and farmers when choosing a specific type of SAPs as an agricultural soil amendment.

The world was ambushed in 2019 by the COVID-19 virus which affected the health, economy, and lifestyle of individuals worldwide. One way of combating such a public health concern is by using appropriate, rapid, and unbiased diagnostic tools for quick detection of infected people. However, a current dearth of bioinformatics tools necessitates modeling studies to help diagnose COVID-19 cases. Molecular-based methods such as the real-time reverse transcription polymerase chain reaction (rRT-PCR) for detecting COVID-19 is time consuming and prone to contamination. Modern bioinformatics tools have made it possible to create large databases of protein sequences of various diseases, apply data mining techniques, and accurately diagnose diseases. However, the current sequence alignment tools that use these databases are not able to detect novel COVID-19 viral sequences due to high sequence dissimilarity. The objective of this study, therefore, was to develop models that can accurately classify COVID-19 viral sequences rapidly using protein vectors generated by neural word embedding technique. Five machine learning models; K nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), Linear discriminant analysis (LDA), and Logistic regression were developed using datasets from the National Center for Biotechnology. Our results suggest, the RF model performed better than all other models on the training dataset with 99% accuracy score and 99.5% accuracy on the testing dataset. The implication of this study is that, rapid detection of the COVID-19 virus in suspected cases could potentially save lives as less time will be needed to ascertain the status of a patient.

Cell immobilization in polymers have proven successful in protecting the nitrogen-fixing bacteria Rhizobium. This study evaluated the feasibility of using lignin to develop lignin-alginate beads with a starch additive to immobilize and release Rhizobial cells. A lignin-alginate hydrogel was synthesized and cultured at different concentrations with 1 ml inoculum of Rhizobium meliloti and Rhizobium leguminosarum to determine the hydrogel's compatibility with the Rhizobium spp. The Rhizobium cells (3 ml inoculum) were then entrapped into the lignin-alginate beads (ratio of 2 g lignin to 1 g alginate) with starch additive and their entrapment efficiency, cell release and surface morphology investigated. The results suggest concentration of the lignin-alginate hydrogel had no effect on the survival of Rhizobium cells with time. Dried lignin-alginate beads increased the survival of Rhizobium cells from 61% to 73% while dried ligninalginate beads with starch additive increased the survival of Rhizobium cells from 61% to 84% compared to only alginate beads. Light microscopy suggests alginate beads lost their sphericity without lignin and starch additive while fixed SEM images highlighted Rhizobium cells attached to starch granules. Overall, the results indicate the potential applicability of lignin as a component for the manufacture of carrier materials for entrapping Rhizobial cells.

Superabsorbent polymers (hydrogels) have been studied for their ability to influence soil hydraulic conductivity because they can store and release water due to their swelling properties. However, concerns related to the increased use of synthetic hydrogels necessitates a switch to bio-based hydrogels, which are renewable and more biodegradable in comparison to synthetic hydrogels. In this study, we synthesized a lignin-based hydrogel and amended a silt loam soil with it at concentrations of 0, 0.1, and 0.3% (w/w). A laboratory permeameter, double membrane tension infiltrometer, and evaporation method were used to measure the saturated (Ks), near saturated, and unsaturated hydraulic conductivity (K) of the samples, respectively. Saturated hydraulic conductivity was significantly decreased by the application of hydrogel at 0.1 and 0.3% (w/w) in comparison to the control treatment. The application of 0.3% (w/w) lignin-based hydrogel only significantly decreased hydraulic conductivity at −1 cm soil water pressure head. Hydraulic conductivity in the 0.1 and 0.3% (w/w) treatments increased along the K(θ) curve in the unsaturated zone (−750 cm < h < −10 cm) in comparison to the control treatment, which we hypothesized was due to bound water in the hydrogel being released and creating a wider path for the movement of water. The 0.1 and 0.3% hydrogel treatments also tended to store more water than the control treatment, especially after 24 h of evaporation. The implication of this study is that lignin-based hydrogels could swell and retain water in saturated soils and the bound water could be released to enhance the flow of soil water in unsaturated soil, thereby reducing the water stress of plants, which require less energy to move and absorb water.

With the growing number of datasets to describe greenhouse gas (GHG) emissions, there is an opportunity to develop novel predictive models that require neither the expense nor time required to make direct field measurements. This study evaluates the potential for machine learning (ML) approaches to predict soil GHG emissions without the biogeochemical expertise that is required to use many current models for simulating soil GHGs. There are ample data from field measurements now publicly available to test new modeling approaches. The objective of this paper was to develop and evaluate machine learning (ML) models using field data (soil temperature, soil moisture, soil classification, crop type, fertilization type, and air temperature) available in the Greenhouse gas Reduction through Agricultural Carbon Enhancement network (GRACEnet) database to simulate soil CO2 fluxes with different fertilization methods. Four machine learning algorithms—K nearest neighbor regression (KNN), support vector regression (SVR), random forest (RF) regression, and gradient boosted (GB) regression—were used to develop the models. The GB regression model outperformed all the other models on the training dataset with R2 = 0.88, MAE = 2177.89 g C ha−1 day−1, and RMSE 4405.43 g C ha−1 day−1. However, the RF and GB regression models both performed optimally on the unseen test dataset with R2 = 0.82. Machine learning tools were useful for developing predictors based on soil classification, soil temperature and air temperature when a large database like GRACEnet is available, but these were not highly predictive variables in correlation analysis. This study demonstrates the suitability of using tree-based ML algorithms for predictive modeling of CO2 fluxes, but no biogeochemical processes can be described with such models.

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