Abstract. Superabsorbent polymers (hydrogels) have been proposed as soil amendments to increase plant available water in soil. Synthetic hydrogels have been widely investigated for use in agriculture. Due to increasing environmental concerns related to synthetic hydrogels, hydrogels from natural sources which are more degradable and biocompatible compared to synthetic hydrogels are being developed. Here, a lignin-based hydrogel was synthesized. The swelling properties of the hydrogel were determined in different aqueous solutions and in soil. Fourier Transform Infrared (FTIR) spectroscopy was used to characterize the hydrogel. Using the hanging water column and the pressure plate method, the soil water retention curve was measured from a soil water pressure head range of -3 cm to the permanent wilting point i.e -15,000 cm for a silt loam soil. For this purpose, the soil was amended with the lignin-based hydrogel at rates of 0, 0.1 and 0.3% (w/w) concentration. Results of the swelling properties of the lignin-based hydrogel show a maximum swelling ratio of 2030% of the hydrogel‘s original mass in deionized water, 1092% in tap water, and 825% in the 0.9% NaCl solution. FTIR spectra of the hydrogel show the presence of O-H bonds which come from the lignin structure and render the hydrogel reactive to water molecules causing swelling as a result. Lignin hydrogel treatment significantly increased water retention near saturation compared to a control treatment of soil with no added lignin hydrogel.
Abstract In the wake of growing concerns regarding diet‐related health issues, this study investigates the application of machine learning methods to estimate the energy content and classify the health risks of foods based on the USDA National Nutrient Database. The caloric content of foods was estimated using the nutritional composition (i.e., carbohydrates, protein, total lipid, and total sugar content) and classified based on their weighted health risks, considering their carbohydrate, lipid, and glycemic index levels. The algorithms used for modeling include multiple linear regression (MLR), K ‐nearest neighbors, support vector machine, random forest regression (RFR), gradient‐boosted regression, decision trees (DT), and deep neural networks. The MLR model demonstrated high accuracy on the training dataset ( R 2 = 0.99, mean absolute error [MAE] = 7.71 kcal, and root mean squared error [RMSE] = 17.89 kcal) and testing dataset ( R 2 = 0.99, MAE = 7.75 kcal, and RMSE = 18 kcal) in energy estimation, indicating its effectiveness in dietary assessment. The RFR and DT models were useful in categorizing foods into low‐health‐risk foods, but their performance was reduced in medium and high‐health‐risk groups. This research contributes to developing tools that could aid in personalized dietary planning and public health interventions to mitigate diet‐related health risks. Practical Application This study applies machine learning to estimate how many calories are in food and to understand the health risks different foods might have. By investigating the fats, cholesterol, and sugars in food items listed in a public database, we can better plan diets or develop apps that help people make healthier eating choices. This work aims to improve public access to nutritional information, supporting efforts to combat diet‐related diseases through educational materials and applications that guide dietary choices in various settings.
Highlights A lignin-based hydrogel was synthesized and shown to possess a swelling ratio of 2013%. The hydrogel contained important hydrophilic hydroxyl groups and macropores for water retention. The hydrogel improved soil water retention in silt loam soil at high matric potentials and in the dry soil range. Increasing hydrogel concentration increased water retention in a loamy fine sand soil at high and low matric potentials. Abstract. Superabsorbent polymers (hydrogels) have been proposed as soil amendments to increase the amount of plant-available water in the soil. Synthetic hydrogels have been widely investigated for use in agriculture. Due to increasing environmental concerns related to synthetic hydrogels, naturally sourced hydrogels are of interest because of their potential for increased biodegradability and biocompatibility. A lignin-based hydrogel was synthesized for this study, and its swelling properties and water absorption capacity were determined. The hydrogel was characterized using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and gas pycnometry. A hanging water column, pressure plate apparatus, and dew point potentiometer were used to measure the soil water retention curve from saturation to oven-dryness for silt loam and loamy fine sand soils after amendment with the lignin-based hydrogel. Results showed a maximum swelling ratio in deionized water of 2013% of the hydrogel’s original mass, 1092% in tap water, and 825% in a 0.9% NaCl solution. The FTIR spectra of the hydrogel showed the presence of O-H bonds from the lignin structure, which renders the hydrogel reactive to a crosslinker and forms insoluble bonds, thereby allowing the hydrogel to swell when exposed to water. SEM images of the lignin hydrogels indicate large macropores, which allowed for water absorption. Applying hydrogels significantly increased the soil's water-holding capacity at 0.3% (w/w) treatment. Hydrogel treatment significantly increased water retention at saturation or near saturation by 0.12 cm3 cm-3 and at field capacity by 0.08 cm3 cm-3 for silt loam soil at 1% (w/w) treatment compared to the control treatment with no added lignin hydrogel. Hydrogel application increased water retention over the range of the soil water retention curve from -3 to -15,000 cm for the loamy fine sand soil at 1% (w/w) treatment. However, the application of lignin-based hydrogel did not affect plant available water capacity (PAWC) in either soil tested. These results serve as preliminary evidence upon which further lignin-based hydrogel amendment studies could be built by testing higher concentrations of hydrogel in the soil. Keywords: Lignin, Soil water retention curve, Super absorbent polymers, Swelling capacity, Water retention.
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.
Abstract 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.
Abstract 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 lignin–alginate 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.
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.
Highlights In this study, six machine learning (ML) models were developed using a large database of soils to predict saturated hydraulic conductivity of these soils using easily measured soil characteristics. Tree-based regression models outperformed all other ML models tested. Neural networks were not suitable for predicting saturated hydraulic conductivity. Clay content, followed by bulk density, explained the highest amount of variation in the data of the predictors examined. Abstract . One of the most important soil hydraulic properties for modeling water transport in the vadose zone is saturated hydraulic conductivity. However, it is challenging to measure it in the field. Pedotransfer Functions (PTFs) are mathematical models that can predict saturated hydraulic conductivity (Ks) from easily measured soil characteristics. Though the development of PTFs for predicting Ks is not new, the tools and methods used to predict Ks are continuously evolving. Model performance depends on choosing soil features that explain the largest amount of Ks variance with the fewest input variables. In addition, the lack of interpretability in most “black box” machine learning models makes it difficult to extract practical knowledge as the machine learning process obfuscates the relationship between inputs and outputs in the PTF models. The objective of this study was to develop a set of new PTFs for predicting Ks using machine learning algorithms and a large database of over 8000 soil samples (the Florida Soil Characterization Database) while incorporating statistical methods to inform predictor selection for the model inputs. Of the machine learning (ML) models tested, random forest regression (RF) and gradient-boosted regression (GB) gave the best performances, with R2 = 0.71 and RMSE = 0.47 cm h-1 on the test data for both. Using the permutation feature importance technique, the GB and RF regression models showed similar results, where clay content described the most variation in the data, followed by bulk density. The implication of this study is that, when predicting Ks using the Florida Soil Characterization Database, priority should be given to obtaining quality data on clay content and bulk density as they are the most influential predictors for estimating Ks. Keywords: Deep learning, Gradient boosted regression, Pedotransfer functions, Random forest regression, Soil database, Soil properties.
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.
