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Article

Comparative Analysis of Japanese Soils: Exploring Power Generation Capability in Relation to Bacterial Communities

1
Graduate School of Bio-Applications and System Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei 184-8558, Tokyo, Japan
2
Institute of Global Innovation Research, Tokyo University of Agriculture and Technology, 3-8-1 Harumi-cho, Fuchu 183-8538, Tokyo, Japan
3
Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, 1-5 Hangi-cho, Simogamo, Sakyo-ku, Kyoto 606-8522, Japan
4
Department of Applied Chemistry, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei 184-8558, Tokyo, Japan
5
Division of Art and Innovative Technologies, K and W Inc., 1-3-16-901 Higashi, Kunitachi 186-0002, Tokyo, Japan
6
Advanced Capacitor Research Center, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei 184-8558, Tokyo, Japan
7
Institute of Agriculture, Tokyo University of Agriculture and Technology, 3-5-8 Saiwai-cho, Fuchu 183-8509, Tokyo, Japan
*
Authors to whom correspondence should be addressed.
Sustainability 2024, 16(11), 4625; https://doi.org/10.3390/su16114625
Submission received: 15 April 2024 / Revised: 23 May 2024 / Accepted: 25 May 2024 / Published: 29 May 2024
(This article belongs to the Section Energy Sustainability)

Abstract

:
This study explores the complex relationship between soil electricity generating capacity, bacterial community dynamics, and soil chemical and physical properties across diverse regions of Japan. First, soil samples were systematically collected and analyzed. Subsequent investigations evaluated soil microbial biomass carbon, dissolved organic carbon (DOC), and total dissolvable iron (DFeT) concentrations. In the experiments, soil samples underwent a rigorous 60-day microbial fuel cell trial, wherein power density and total energy output were measured. Significant variations in power density were observed among different soil samples; specifically, a sugarcane field designated as Okinawa-3 and a peach orchard soil as Nagano-2 demonstrated relatively high total energy output. Analysis of soil bacterial community structures identified some families which showed positive correlations with increased electricity generation capabilities. Correlation analyses revealed associations between these bacterial communities and key soil parameters, particularly with DOC and DFeT concentrations. Redundancy analysis revealed intricate connections between soil properties and electricity generation capacities. Particularly noteworthy was the positive correlation between Acidobacteriaceae and DOC, as well that between Sphingomonadaceae and electricity generation, highlighting the crucial roles of soil microbial communities and chemical compositions in driving electricity generation processes.

1. Introduction

Energy is any power source utilized for the development and operation of human society [1]. At present, the energy consumed is mainly fossil energy, such as coal, oil, and gas. Although these traditional fossil energy sources temporarily meet our needs, they have also brought an increasingly serious threat to the environment, and the energy crisis caused by the excessive consumption of fossil energy has become an urgent problem. With the depletion of fossil fuels, it is crucial to develop renewable energy sources, such as wind, solar, hydropower, biomass, and geothermal energy [2,3,4].
Microbial fuel cells (MFCs) are a bio-electrochemical system that produce renewable energy [5] by using microbial metabolism to catalyze the oxidation/reduction reaction of bioelectrodes and convert organic matter and biomass in substrates, such as wastewater, hydrogen or methane, into electric energy [6]. MFCs are inexpensive, have no secondary pollution concerns, and have a wide range of research and application possibilities [7,8]. In an MFC system, organic matter is broken down by microbes releasing electrons and protons. Electrons are transferred through a suitable medium and transmitted to the cathode through an external circuit to form a current [9,10]. The energy output and recovery of the MFC system mainly depend on its structure and the microbial community on the electrode surface. In recent years, the energy conversion and power generation of MFCs have been significantly improved through the continuous improvement of MFC structures. However, improvements in the reactor structure will also alter the microbial community of the biofilm on the surface of the anode and cathode; therefore, the physiology and ecology of electroactive microorganisms must be taken into account when designing the reactor to fully understand the efficiency of the system [11]. Prioritizing the optimal environment required for electroactive microbes is essential in designing despite potential limitations due to the fixed structure of the reactor.
Microbial flora are an indispensable part of MFC systems and have a significant impact on the electricity generation performance [12]. The microbial community of an MFC system is divided into two main categories: electrochemically active bacteria (EAB) with electron transfer ability and non-EAB with only an intracellular respiratory function. The performance of microorganisms in MFC systems primarily depends on the species and abundance of microbial communities [13]. Several studies have shown the significant impact of microbial communities on the power output of MFC systems. Anaerobic microbial community types and continuous feeds of substrate can facilitate the evolution microbial communities suitable for MFC systems. Different microbial communities have been established in biodynamically connected MFC systems at the anode, with a decrease in the abundance of fermentative communities and an increase in the abundance of respiratory communities as the cascade progressed. Such increased performance suggests a mechanism of metabolic interactions between the dominant fermentative-like community and the late anaerobic respiratory electron acceptor [14,15]. It has been shown that an increase in performance is associated with an increase in microbial diversity [16] and the number of anaerobic respiratory bacteria [17]. Meanwhile, metabolic interactions within microbial communities have been found to be crucial for efficiently degrading complex organic compounds, ultimately determining community function, activity, and stability, and underpinning microbially driven natural phenomena, such as the cycling of carbon, nitrogen, and sulfur components [18,19,20].
The purpose of the present study is to investigate the relationship between electricity generation capacity, microbial community, and soil chemistry in soils from various agricultural fields of Japan. In most MFC systems, the substrate for EAB is wastewater [21], whereas in microbial batteries in agricultural field soils, the substrate is the soil organic matter. The organic matter in soils is fed by exudation from plant roots, which are derived from fixed carbon by photosynthesis; thus, this system is environmentally sustainable [22]. We will combine this system with a supercapacitor that can effectively store relatively weak electricity [23] to make microbial batteries in the future. An MFC system combined with a paddy field has also been reported [24]. In contrast, the target fields for our microbial battery are dry fields as well as paddy fields, aiming to apply the microbial battery in future agriculture (power supply for sensors) both in dry and paddy fields.
The processes used to generate electricity from soils currently have several potential applications. MFC systems are used to mitigate methane emissions from paddy soil and sediments, monitor the toxicity of contaminants and soil microbial activity [25,26,27], and remove hazardous substances, such as phenol, gasoline, and petroleum, from soils [28,29].
In the present study, soil samples were collected from agricultural fields in Japan. While investigating the relationship between the power generating force, bacteria and soil properties, the power outputs of paddy field and -upland field soils were also compared. Using high-throughput 16S rRNA sequencing technology, metagenomics and redundancy analyses were performed to evaluate the effects of different soil properties on the composition of microbial communities with different power generating densities. Finally, we identified the high diversity of microorganisms and their optimal living environment.

2. Materials and Methods

2.1. Soil Sampling and Analysis

Subsurface soils (10–20-cm soil depth) were collected from several parts of Japan (Hokkaido, Akita, Fukushima, Nagano, Tokyo, Kyoto, and Okinawa prefectures). The soil samples were collected from four corners and five points in the center of a 0.5 m × 0.5 m area, then blended to create a composite sample. Following the removal of rocks and crop residues, these samples were combined to represent a single specimen. After passing through a 2 mm diameter sieve, soil samples were stored at 4 °C for up to 1 month before being subjected to analysis with MFCs.
The physicochemical properties of the soil components were assessed using conventional methods before and after the MFC measurements [30]. Soil texture was determined using the sieve and pipette method [31], while soil moisture content was determined quantitatively. The maximum water holding capacity (MWHC) of the soil was determined using the Hilgard method, which involves weighing the dry and soaked soil [32]. Soil pH and electrical conductivity (EC) were measured at a soil/water ratio of 1:5 (soil/water). Soil total carbon (TC) and total nitrogen (TN) were measured using an N.C-ANALYZER (SUMIGRAPH®NC-TR22) with C5H9NO4 as a standard. Available sulfur (AvS) was determined using the calcium dihydrogen phosphate extraction method [33]. The fumigation-extraction method [34] combined with 0.5 M K2SO4 was used, with Shimadzu Corporation’s TOC series instrument, to measure soil microbial biomass carbon (MBC), dissolved organic carbon (DOC), and dissolved organic nitrogen (DON). Soil total dissolved iron (DFeT) was also measured using the acidic oxalate extraction method [35].

2.2. Microbial Fuel Cell Assembly

The mud was poured into the Mud-WattTM Microbial Fuel Cell Kit (Keego Technologies, Palo Alto, CA, USA), with sterile water purified with a reverse osmosis membrane (RO water) added to the soil sample according to the manufacturer’s instructions. The negative electrode carbon felt was positioned at the bottom of the device, followed by the addition of soil. Subsequently, the positive electrode carbon felt was placed on top, ensuring that the water level on the surface reached approximately 1 cm above the cathode. After setting up the devices, the samples were incubated at 28 °C under dark. The currents and voltages of the electricity generated from the MFCs were recorded every 48 h for 60 days. Sterile RO water was added to the maximum water holding capacity of each soil and then every 5 days to compensate for water evaporation and maintain the initial conditions. After 60 days of operation, each soil sample was sampled from the middle of the MFC for the analysis of their chemical properties, and near the anode for amplicon analysis. Parts of the soils near the anodes were subjected to amplicon analysis, as described in the following section.
The power density of the MFCs for each soil sample was calculated by dividing the product of the measured current and voltage by the electrode area. The total energy generated by the MFCs over 60 days was estimated as the area under the curve of the Power Density vs. Time plot (Figure S1). Specifically, this was calculated by dividing the area under the curve into trapezoids as follows: A = h 2 (y0 + 2y1 + 2y2 + … +2yn−1 + yn), where A represents the approximate area under the curve. h denotes the width of each trapezoid calculated as b a n where a , b are the interval. y0, y1, y2, …, yn are the values at the given data points.

2.3. DNA Extraction and Amplicon Analysis

For revealing direct correlations between electricity generations and microbial community compositions, high-throughput sequencing of 16S rRNA was performed on soil samples collected near the anode after the MFC analysis by Bioengineering Lab. Co., Ltd. (Sagamihara, Kanagawa, Japan). DNA was extracted from 0.5 g of soil using ISOIL (NIPPON Genetics Co., Ltd., Tokyo, Japan) according to the manufacturer’s protocol. The V3/V4 regions of the bacterial 16S rRNA were amplified using the PCR primers V3V4f_MIX and V3V4r_MIX (Bioengineering Lab. Co., Sagamihara, Kanagawa, Japan). The first PCR was performed using the ExTaq® enzyme (Takara bio, Shiga, Japan) under the following conditions: 94 °C for 2 min, followed by 30 cycles of amplification at 94 °C (30 s), 55 °C (30 s), 72 °C (30 s), with a final extension step of 7 min at 72 °C. The PCR products were purified using the FastGene Gel/PCR Extraction Kit (NIPPON Genetics Co., Ltd., Tokyo, Japan). Tailed PCR was conducted using 2 µL of the purified PCR product, according to the manufacturer’s instructions. The PCR product was purified using the same kit as in first PCR reaction. The PCR products were quantified using Synergy H1 (BioTek, Winooski, VT, USA) and QuantiFlour dsDNA system. A fragment analyzer and dsDNA 915 reagent kit (Advanced Analytical Technologies, Inc., Ames, IA, USA) were used to evaluate library quality. The sequences were analyzed using the MiSeq system and MiSeq Reagent Kit ver 3.0 (Illumina, Foster, City, CA, USA). The raw high-throughput sequencing data were then processed using Quantitative Insights into Microbial Ecology (QIIME2.0). Finally, the processed sequencing data were analyzed in R software (version 4.2.2) using the packages Ampvis2, vegan, and ggplot2. Alpha and beta diversity were analyzed using Microbiome Analyst 2.0.

2.4. Nucleotide Sequence Accession Numbers

The sequencing reads used for amplicon analysis were submitted to DRA/DDBJ under the accession number DRA016234.

2.5. Statistical Analyses

To visualize bacterial community clusters, we used Principal Coordinate Analysis (PCoA) with the Bray-Curtis distance (PERMANOVA) method. We also used Spearman’s rank correlation to evaluate the relationship between soil discharge capacity, soil properties, and microbial community structure in redundancy analysis (RDA).

3. Results

3.1. Soil Properties before and after MFC Operation

The sampling site location, sample texture, and physical properties of the 18 soils are shown in Table 1. The sand and silt contents of the five soil samples from Okinawa were lower than those of other samples, while their clay content was higher. The physicochemical and microbial properties of the soils are listed in Table 2. pH values varied from 5.2 in Okinawa-1 to 7.7 in Okinawa-2. The TC and TN values in the Hokkaido-1 soils were the highest and those in the five soil samples from Okinawa were relatively lower than that of other areas. The AvS content in Okinawa-5 was more than threefold higher than that in any other samples. Nagano-2 had the highest content of DFeT before MFC operation, followed by Nagano-1. Soils from Okinawa-1 exhibited the highest soil DOC both before and after MFC operation. As for microbial properties, Nagano-4 exhibited the highest MBC both before and after MFC operation. MBC after MFC operation decreased to less than half that from before MFC operation in the Okinawa-3, Okinawa-4, and Nagano-2 samples.

3.2. Power Generation of Soils in MFCs

The power densities for 60 days of MFC operation are shown in Supplementary Figure S1 and their total generated energy in Figure 1. The total generated energy of the 18 isolates over the 60 days ranged from 0.05 to 57.9 Wh/m2. Among the five soil samples from Okinawa, which have essentially the same physical and chemical properties, Okinawa-2 and Okinawa-4 had very low peak power densities at 0.15 and 2.5 mW/m2, respectively. In contrast, Okinawa-1, Okinawa-3, and Okinawa-5 showed relatively higher density peaks, and the Okinawa-3 soil showed the highest total generated energy of 53.9 Wh/m2 among all other soil samples. Among the paddy field soils, Akita and Fukushima-1 exhibited higher total generated energy than the others, e.g., 8.6 and 7.8 Wh/m2, respectively. In contrast, in Nagano-5, a natural farming paddy without chemical fertilizer nor chemical herbicide, the total generated energy was the lowest at 0.05 Wh /m2. Although the samples of Nagano-2 and Nagano-3 were from the same orchard field, different fruits were grown (peach and apple, respectively), and Nagano-2 showed the highest total generated energy (57.9 Wh/m2) compared to that in Nagano-3 (11.0 Wh/m2).

3.3. Analyses of Bacterial Community Structure

To determine the bacterial community structure in which the soil exhibits higher electricity generation, we performed amplicon analysis using bacterial 16S rRNA. A total of 312,023 bacterial 16S rRNA gene sequences were generated after filtering, quality control, and removal of chloroplast and mitochondrial OTUs (Table S1). The bacterial rarefaction curves are shown in Figure S2.
The α-diversity of the bacterial community was calculated based on microbial diversity (Shannon index) and richness (Chao I) at the OTU level (Table S2). The soil samples were single replicates from 18 locations, and statistical analysis could not be performed. However, we found that both the Shannon and Chao I indices of the soil of Okinawa-1, Okinawa-2, and Akita tended to be lower than that of the other samples.
The relative abundances of the top 20 bacterial classes are shown in the heat map (Figure 2a), which reveals that α-proteobacteria, β-proteobacteria, and Sphingobacteria were more abundant in the samples. Okinawa-1, Okinawa-3, Nagano-1 and Nagano-2 exhibited higher power density. The top 3 taxa in Okinawa-1 were Acidobacteria (21.7%), Solibacteres (12.3%), and Spartobacteria (10.8%), while those in the Okinawa-3 sample were α-proteobacteria (20.3%), Acidobacteria (14.2%), and Sphingobacteria (9.7%). Although the relative abundance of Acinobacteria was higher in the Okinawa samples, Planctomycetia and Latescibacter were lower in abundance (0–0.2%). Conversely, the relative abundance of Planctomycetia and Latescibacter in Nagano-2 were 4.4% and 2.4%, respectively. The top 3 taxa were α-proteobacteria (10.2%), Vicinamibacter (8.9%), and γ-proteobacteria (8.1%) in Nagano-2, Acidobacteria (14.4%), Sphingobacteria (12.6%) and Vicinamibacter (8.1%) in Nagano-1. The heatmap of the top 20 families is shown in Figure 2b. Okinawa-1 and Okinawa-3 contained the most Acidobacteriaceae among the five Okinawa samples. However, the specific families are not shown in the Nagano samples. These results indicated that the relative abundance of bacteria differed greatly between Okinawa and Nagano, and the bacteria that produced a high output of electricity were different in each soil. Due to substantial differences in the physical, chemical, and biological properties of soil samples from the Okinawa region compared to others, we opted to separately compare the bacterial community characteristics of Okinawa’s soil with those of other regions. The optimal habitat for soil bacteria varies depending on the region [36]. β-diversity showed significant differences between Okinawa samples and others. These results suggest that the Okinawa samples have a specific bacterial community structure (Figure 3). The abundance of exo-electrogenic bacteria [37] across 18 samples indicates that, despite geographic variability, rice paddy soils demonstrate consistent microbial compositions (Figure 4). The prevalence of Geobacter across diverse locations underscores its pivotal role in electricity generation. Particularly noteworthy is the presence of Geobacillus (OTU2068) in soils with high power generation, such as Okinawa-1 and Okinawa-3, indicating its potential contribution to enhanced electrical output. In contrast, soils from Nagano-1 and Nagano-2 were characterized by a predominance of Pseudomonas.
We performed RDA to clarify the factors that contributed to electricity production using soil properties and bacterial communities showing more than 1% relative abundance. First, we performed RDA using 18 soil samples (Figure 5a) and observed positive correlations between electricity generation, Acidobacteriaceae, and the Okinawa-1 and Okinawa-3 soils. In addition, the DOC may have contributed to those samples. Interestingly, only the arrow line of DON shows a trend opposite to that of RDA2 before and after MFC operation. A Spearman’s rank correlation test on the relative abundances of the bacterial family and soil chemical properties showed a weak correlation between Acidobacteriaceae and electricity generation (Table S3). Four families, Sphingomonadaceae, Tepidisphaeraceae, Bradyrhizobiaceae, and Steroidobacter, showed a moderate correlation to the generated electricity, while Solibacteraceae and Sphingobacteriaceae showed a weak positive correlation. Acidobacteriaceae showed a positive correlation with DOC (before and after) and MBC (only after) but a strong negative correlation with pH values.
The β-diversity of the Okinawa samples differed greatly from that of the other samples (Figure 3). Moreover, Okinawa-3, which showed the highest electricity production, strongly affected this RDA (Figure 5a). We then performed RDA on the samples except for those from Okinawa (Figure 5b) and found that Sphingomonadaceae positively correlated with the amount of generated electricity. The Spearman’s rank correlation test revealed that eight genera (Chitinophagaceae, Vicinamibacter, Sphingomonadaceae, Tepidisphaeraceae, Pedosphaera, Pseudomonadaceae, Steroidobacter, and Blastocatellaceae) showed a positive correlation with electricity generation (Table S4). Here, three genera (Sphingomonadaceae, Tepidisphaeraceae, and Pseudomonadaceae) showed a positive correlation with DFeT and DOC, but not DON. Pseudomonadaceae in particular had the highest correlation to energy generation, but the correlation between DFeT and DOC was weaker than that of the other two genera. Dendrogram analysis showed that Nagano-2 was similar to that of the Kyoto sample (Figure S4). The electricity generation of the Kyoto sample was less than one-sixth that in Nagano-2 (Figure 1). However, the RDA of both Nagano-2 and Kyoto showed similar trends (Figure 5b).
Focusing on Okinawa, the results of the RDA indicated that Okinawa contributed positively to DOC and Acidobacteriaceae (Figure S3). The positive correlation between Sphingobacteriaceae and electricity use was not significant (Table S5). A thorough analysis of Acidobacteriaceae abundance in 18 soil samples revealed a significant predominance in Okinawa, with the Koribacter species being notably prevalent (Figure S5). These findings imply that the bacterial composition influencing electricity generation varied across the different soil samples. Although the soil contained different types of bacteria, the genera that correlated with high electricity generation had a positive relationship with DOC.

4. Discussion

This study seeks to investigate the interrelationship between electricity generation, microbial community structure, and soil chemical properties across diverse regions of Japan. Soil samples were systematically collected from agricultural fields throughout Japan, and high-throughput 16S rRNA sequencing technology and diversity analysis were employed to assess the impact of different soil properties on microbial community composition. Furthermore, comparative analyses were conducted to assess the electricity outputs of soils from both paddy fields and dry fields. The overarching objective is to delineate microbial diversity patterns and identify their optimal ecological niches, thereby fostering a deeper understanding of the potential applications of microbial batteries in future agricultural contexts.
Utilizing paddy field MFCs to generate electricity is thought to be suitable for sediment MFCs, and rice paddy MFCs generate maximum electric power as high as ~80 mW/m2 (based on the projected anode area) [24,38]. In our study, the microbial community structures of the paddy soils were found to be distinct from upland soils (Figure S4). Among the paddy soils, Akita and Fukushima-1 exhibited relatively higher total electricity generation and max power density (Figure 1, Figure S1), respectively. Also, Akita and Fukushima-1 showed higher maximum water holding capacity and lower DFeT (Table 1, Table 2). The number of read counts of Geobacter, well-known electricity-generating bacteria, was detected mainly in the paddy soils (Figure 4). Additionally, RDA analysis showed a clear division in the microbial community structure between the paddy soils and the others by RDA1 (Figure 5b). It is evident that paddy soils harbor distinct microbial communities and soil properties, such as DFeT and water holding capacity, which differ from those of other upland soils, and these communities have a significant impact on energy generation.
Iron powder treatment was found to be effective in enhancing electricity generation in MFCs by lowering the redox potential and promoting the growth of anaerobes [39]. Generally, exo-electrogenic bacteria can reduce Fe (III), and Desulfobulbus uses both sulfate and Fe (III) as an electron acceptor [40]. Only the Akita and Fukushima-1 samples contained Desulfobulbus (Figure 4), suggesting that Desulfobulbus may have contributed to the electricity generation of the paddy soils.
Okinawa-1, Okinawa-3, Nagano-1, and Nagano-2 soils generated the highest amount of electricity (Figure 1). RDA showed that electricity generation strongly correlates with DOC (Figure 5a) or DFeT (Figure 5b), within families that showed a positive correlation with electricity generation (Tepidisphaeraceae, Sphingomonadaceae Bradyrhizobiaceae, Steroidobacter, and Rhodospirillaceae). A previous paper indicated that the members of Bradyrhizobiaceae were known as electrogenic bacteria at the family classification level [41]. Sphingomonadaceae showed a positive correlation with DFeT and DOC (Table S4). Although Pseudomonadaceae also correlated with electricity generation, it did not show any correlation with DFeT and DOC (Table S4). Interestingly, the DON of Okinawa-3, Nagano-1, and Nagano-2, which showed high energy generation, increased by more than two times after MFC (Table 2). These results underscore the importance of specific soil chemical properties, such as DOC and DFeT, and microbial families’ presence as pivotal determinants in optimizing electricity generation in MFCs.
The abundance showed that the exo-electrogenic bacteria contained Bacillus, Geobacillus, and Pseudomonas (Figure 4). Pseudomonas species secrete phenazine as a mediator of electron transfer [42]. Numerous studies on the electricity generation of Geobacillus have shown that the thermophilic Geobacillus sp. strain WSUCF1 generates electricity directly from corn stover and food waste using its unique pathways of lignocellulolytic reaction, biofilm formation, and transmembrane and extracellular electron transfer pathways [43]. Based on the analysis of Spearman’s Rank correlation coefficients and p-values (Table S6), a highly significant positive correlation was observed between Geobacillus and power density, suggesting a strong positive relationship between the presence of Geobacillus and power density within the soil microbial community. The significance of this correlation implies that Geobacillus may play a crucial role in soil electrochemical processes. Geobacillus was found in the soils which generated the highest amount of electricity, Okinawa-1, 3 and Nagano-1, 2, as well as in Okinawa-5. We could not determine why Okinawa-5 generated less electricity, but it may be due to its pH, EC, AvS, and DON which were higher than that of the other samples (Table 2). Except for Okinawa-5, this result suggests the possibility that Geobacillus contributes to electricity generation.
The Principal Coordinate Analysis (PCoA) revealed that the microbial community structures of Okinawa soils differed greatly from other soils (Figure 3). Among the Okinawa soils, those with high electricity generation contained high DOC content and had a weakly acidic pH (Table 2). RDA and Spearman’s correlation showed that Acidobacteriacea is strongly correlated with DOC (Figure 5 and Table S5). Regarding the content of the Acidobacteriacea, we found Koribacter in the Okinawa-1 and Okinawa-3 samples (Figure S5). While information on Koribacter is limited, these bacteria are known to use iron for their growth [44], so it is possible that Koribacter and another member of Acidobacteriacea exhibit exo-electrogenic activity.
Previous studies have shown that plants affect soil properties and microbial communities [45,46,47]. Plant roots secrete various organic compounds, such as proteins, sugars, flavonoids, amino acids, and organic acids by photosynthesis. These compounds affect the microbial community structures, and the soil properties and microbes affect electricity generation. Jiang et al.’s study suggested that DOC (or OC) is a more effective factor for electricity generation than pH [48]. As in their study, we found that the soil contents of DOC showed a high correlation with electricity. Future studies with combination of plants and soils are needed to clarify the effects of root exudates on electricity generation and the relationship between DOC and microbial community structures.

5. Conclusions

This study underscores the potential of microbial batteries in electricity generation and reveals significant disparities in microbial community structures between paddy and upland soils. The technology of microbial batteries holds substantial promise for advancing the utilization of renewable energy, mitigating dependence on finite resources, and fortifying soil quality and ecosystem stability. Despite their lower power output compared to solar panels, microbial batteries provide practical solutions for various small-scale applications, such as supplying power to devices in areas with limited sunlight, deploying sensors for disaster detection, enhancing IoT agriculture, and serving as crucial emergency power sources.
This research with agricultural soils is fundamental for the development of microbial batteries. Although our current study focuses on energy generation from microbial communities in soils, it lays the groundwork for future studies that will explore the combination of plants and soils.
Furthermore, our results indicate that the level of energy generation can be used to evaluate the cycling of key chemical elements, such as carbon and nitrogen, under natural conditions. This ability to assess elemental cycling is crucial for understanding and managing soil health and fertility. Consequently, microbial batteries with agricultural field soils are expected to play a pivotal role in local sustainable development endeavors, contributing significantly to community sustainability.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16114625/s1, Figure S1: Soil power density changes in MFCs over 60 days; Figure S2: Rarefaction curve for MFC operation of soil samples based on observed OTUs; Figure S3. Family-level redundancy analysis (RDA) of bacterial communities in soil samples for soil physicochemical properties and power density; Figure S4: Dendrogram analysis; Figure S5: The sequence read counts of Acidobacteriaceae in 18 soils; Table S1: Library size of each soil in this study; Table S2: Alpha-diversity of the 18 soils used for MFCs Operation; Table S3: Spearman’s rank correlation coefficients of microbial community abundance and soil chemical properties between 18 soil samples used for RDA analysis in Figure 5a; Table S4: Spearman’s rank correlation coefficients of microbial community abundance and soil chemical properties between 13 soil samples except for Okinawa used for RDA analysis in Figure 5b; Table S5: Spearman’s rank correlation coefficients of microbial community abundance and soil chemical properties between Okinawa soil samples used for RDA analysis in Figure S3; Table S6: Spearman’s rank correlation coefficients between the total number of reads (genus) known as exo-electrogenic bacteria in Figure 4 and the power density.

Author Contributions

Conceptualization, Z.Y., K.Y., M.Y. and N.O.-O.; methodology, Z.Y., K.Y., M.S., K.M., N.O., W.N., K.N., H.T., S.S., M.Y. and N.O.-O.; software, Z.Y., K.Y. and M.Y.; validation, Z.Y., K.Y., M.S., S.S., M.Y. and N.O.-O.; formal analysis, Z.Y., K.Y., M.S., S.S., M.Y. and N.O.-O.; investigation, Z.Y., K.Y., M.S., S.S., M.Y. and N.O.-O.; resources, Z.Y., K.Y., M.Y. and N.O.-O.; data curation, Z.Y., K.Y., M.S., S.S., M.Y. and N.O.-O.; writing—original draft preparation, Z.Y., M.Y. and N.O.-O.; writing—review and editing, Z.Y., K.Y., M.S., S.-I.A., K.M., N.O., W.N., K.N., K.T., H.T., S.S., M.Y. and N.O.-O.; visualization, Z.Y., K.Y., M.Y. and N.O.-O.; supervision, K.Y., M.S., S.-I.A., K.M., N.O., W.N., K.N., K.T., H.T., S.S., M.Y. and N.O.-O.; project administration, N.O., W.N., K.N., S.S., M.Y. and N.O.-O.; funding acquisition, K.N. and N.O.-O. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fund for the Grant-in-Aid for Challenging Research (Exploratory) 22K19171 by JSPS.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We thank Takuro Shinano, Takashi Sato, Masami Yoshikawa, Takashi Motobayashi, Takashi Sato and Tadashi Yokoyama, for giving us soil samples.

Conflicts of Interest

Author Wako Naoi was employed by the company Division of Art and Innovative Technologies, K and W Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. The total generated energy of each soil sample in 60 days.
Figure 1. The total generated energy of each soil sample in 60 days.
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Figure 2. Heat Map of the top 20 bacterial classes (a) and families (b) at 18 different soils in Japan. The heat map showed the relative abundances of bacterial operational taxonomic units (OTUs) at the class level from various locations in Japan. The color of the heat map indicates the relative abundance from high (red) to low (blue).
Figure 2. Heat Map of the top 20 bacterial classes (a) and families (b) at 18 different soils in Japan. The heat map showed the relative abundances of bacterial operational taxonomic units (OTUs) at the class level from various locations in Japan. The color of the heat map indicates the relative abundance from high (red) to low (blue).
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Figure 3. Beta-diversity of bacterial community structure of soil. Principal Coordinate Analysis (PCoA) is plotted based on Bray-Curtis distance (PERMANOVA). The soils collected from Okinawa (red) differed significantly from that of other sites (blue). This differentiation is illustrated by the clear clustering of red and blue symbols, indicating distinct bacterial communities between Okinawa and other locations.
Figure 3. Beta-diversity of bacterial community structure of soil. Principal Coordinate Analysis (PCoA) is plotted based on Bray-Curtis distance (PERMANOVA). The soils collected from Okinawa (red) differed significantly from that of other sites (blue). This differentiation is illustrated by the clear clustering of red and blue symbols, indicating distinct bacterial communities between Okinawa and other locations.
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Figure 4. The total number of reads (genus) known as exo-electrogenic bacteria. The number of reads selected known as exo-electrogenic bacteria [37] in 18 samples was shown.
Figure 4. The total number of reads (genus) known as exo-electrogenic bacteria. The number of reads selected known as exo-electrogenic bacteria [37] in 18 samples was shown.
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Figure 5. Redundancy Analysis (RDA) of bacterial communities in soil samples at family level with soil physicochemical properties and power density. (a) all 18 soil samples. (b) The 13 soils (except for Okinawa) samples. “A” and “B” after DOC, DON, DfeT and MBC are “After” and “Before” MFC operation, respectively.
Figure 5. Redundancy Analysis (RDA) of bacterial communities in soil samples at family level with soil physicochemical properties and power density. (a) all 18 soil samples. (b) The 13 soils (except for Okinawa) samples. “A” and “B” after DOC, DON, DfeT and MBC are “After” and “Before” MFC operation, respectively.
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Table 1. Sampling site location, sample texture and physical properties of the eighteen soils.
Table 1. Sampling site location, sample texture and physical properties of the eighteen soils.
SoilCropFertilization of NPKLocationSandSiltClaySoil MoistureMWHC
(%)(%)(%)(%)(%)
Okinawa-1SugarcaneChemical25°57′7″ N,
31°17′42″ E
0.719.779.622.088.4
Okinawa-2SugarcaneChemical25°56′56″ N,
131°17′17″ E
19.316.564.225.0107.2
Okinawa-3SugarcaneChemical25°56′40″ N,
131°17′21″ E
2.915.681.523.785.9
Okinawa-4SugarcaneChemical25°56′8″ N,
131°17′46″ E
2.317.580.220.896.0
Okinawa-5SugarcaneChemical25°57′6″ N,
131°19′27″ E
2.517.280.323.296.7
KyotoSoybean (Kurodaizu)Compost and chemical35°1′3″ N,
135°34′14″ E
50.232.817.014.479.0
AkitaRice (paddy)Chemical40°1′4″ N,
139°57′35″ E
43.826.729.541.2184.5
Hokkaido-1Rice (paddy)Compost only43°00′48″ N,
141°23′28″ E
42.038.419.538.5155.5
Hokkaido-2RyeCompost and chemical43°5′27″ N,
141°20′27″ E
52.434.113.520.981.4
Fukushima-1Rice (paddy)Compost only37°36′18″ N,
140°34′48″ E
60.322.916.835.0149.5
Fukushima-2VegetableCompost only37°45′1″ N,
40°23′25″ E
45.333.021.735.6100.3
Tokyo-1Rice (paddy)Compost and chemical35°39′57″ N,
139°28′5″ E
31.539.529.132.0117.9
Tokyo-2VegetableCompost and chemical35°39′57″ N,
139°28′35″ E
26.442.231.438.2154.1
Nagano-1VegetableNatural Farming without chemical fertilizer nor chemical herbicide36°11′43″ N,
137°51′3″ E
53.629.716.720.690.9
Nagano-2Peach (orchard)Chemical36°45′11″ N,
138°23′10″ E
45.734.719.621.086.3
Nagano-3Apple (orchard)Chemical36°45′8″ N,
138°22′59″ E
41.734.423.923.894.8
Nagano-4Rice (paddy)Chemical36°45′08.4” N
138°22′58” E
43.137.419.619.094.5
Nagano-5Rice (paddy)Natural Farming without chemical fertilizer nor chemical herbicide36°11′43″ N,
137°51′3″ E
42.938.119.031.6119.9
MWHC: maximum water holding capacity.
Table 2. Soil physicochemical and microbial properties before and after MFC operation.
Table 2. Soil physicochemical and microbial properties before and after MFC operation.
SoilpH
Before
EC
(mS/m)
Before
TC (g/kg)
Before
TN (g/kg)
Before
AvS (mg/kg)
Before
DFeT (g/kg)
Before
DFeT (g/kg)
After
DOC (mg/kg)
Before
DOC (mg/kg)
After
DON (mg/kg)
Before
DON (mg/kg)
After
MBC (mg/kg)
Before
MBC
(mg/kg)
After
Okinawa-15.28.91323912306226556793100
Okinawa-27.713.814217331178934269566
Okinawa-35.713.81824923202165451021587
Okinawa-46.331.111278121561293327238
Okinawa-56.363.5162356121681513191045442
Kyoto6.311.315114226056536310664
Akita7.321.624388335661417551133
Hokkaido-15.933.968610144106103161143316358
Hokkaido-26.75.22621455647019514644
Fukushima-16.227.92429122756710465300232
Fukushima-26.354.34251383420115423792241145
Tokyo-15.931.0384924387134140112257236
Tokyo-26.125.2414894315288126115681463
Nagano-16.819.4474745511210969168412190
Nagano-26.016.036317551108272184252175
Nagano-35.611.146420441041195511723241
Nagano-45.814.6283262366654310210851264
Nagano-55.631.7525184214112218862292442
EC: electrical conductivity, TC, total carbon; TN, total nitrogen; AvS, Available sulfur; DFeT, total dissolved iron; DOC, dissolved organic carbon; DON, dissolved organic nitrogen, MBC, microbial biomass carbon.
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Yue, Z.; Yuan, K.; Seki, M.; Agake, S.-I.; Matsumura, K.; Okita, N.; Naoi, W.; Naoi, K.; Toyota, K.; Tanaka, H.; et al. Comparative Analysis of Japanese Soils: Exploring Power Generation Capability in Relation to Bacterial Communities. Sustainability 2024, 16, 4625. https://doi.org/10.3390/su16114625

AMA Style

Yue Z, Yuan K, Seki M, Agake S-I, Matsumura K, Okita N, Naoi W, Naoi K, Toyota K, Tanaka H, et al. Comparative Analysis of Japanese Soils: Exploring Power Generation Capability in Relation to Bacterial Communities. Sustainability. 2024; 16(11):4625. https://doi.org/10.3390/su16114625

Chicago/Turabian Style

Yue, Zihan, Kun Yuan, Mayuko Seki, Shin-Ichiro Agake, Keisuke Matsumura, Naohisa Okita, Wako Naoi, Katsuhiko Naoi, Koki Toyota, Haruo Tanaka, and et al. 2024. "Comparative Analysis of Japanese Soils: Exploring Power Generation Capability in Relation to Bacterial Communities" Sustainability 16, no. 11: 4625. https://doi.org/10.3390/su16114625

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