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.

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.

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.

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.