Large volumes of total waste generation that are not properly collected and disposed pose a serious environmental and health risk. For instance, [1] reported that Ghana generates 810 tonnes of solid waste daily, but only 216 tonnes are collected, leaving 594 tonnes uncollected and posing serious hazards while Nigeria generates at least 32 million tonnes of solid waste annually. Unfortunately, only 30% of the waste is efficiently collected and disposed due to inadequate waste management services. As a matter of fact, waste generation in countries like Nigeria, Kenya, and Sub-Saharan Africa far exceeds collection and disposal [2].

Unfortunately, improper waste disposal leads to air, soil, and water pollution, exacerbating the greenhouse gas effect and negatively impacting human health, the environment, and the economy [3]. Besides, the growing volume of waste makes continuous landfill disposal unsustainable [4]. Even incineration, which reduces landfill wastes because it involves clearing and burning of materials at high temperatures, generates harmful and hazardous pollutants which are released into the air, and from there contaminate soil and groundwater, thus making landfills antithetical to circular economy. Hence, there is need for efficient solid waste management approach that will reduce contamination and significantly minimise environmental pollutions

Consequently, anaerobic digestion is such a promising method. It is a biological process where microorganisms break down biodegradable organic matter without oxygen in a sealed system called an anaerobic bioreactor, producing biogas for energy and exemplifying the circular economy. The resulting biogas can be used for electricity, heating, or vehicle fuel. Meanwhile, cow dung is a popular substrate for biogas production due to its high organic matter content. Nonetheless, relying solely on cow dung for generating biogas may limit gas yield and process efficiency, necessitating the search for biocatalysts that can optimize digestion process because one of the major constraints in biogas production is low yields and slow digestion rates. Regrettably, the application of plant-based biocatalysts in the optimisation of biogas production has not been well documented. Remarkably, plant materials have been reported by many authors in several literatures to consist of bioactive compounds, eco-friendly, biodegradable, cost-effective and non-toxic, making them a good substitute to chemical catalysts for accelerating the digestion process and improving biogas yield.

On this basis, the objective of this study is to investigate the influence of powders of Azadirachta indica (neem), Curcuma longa (turmeric) and Carica papaya (papaya) on biogas yield and microbial communities during anaerobic digestion of cow dung.

Materials and Method

Preparation of plant powders

Leaves of Carica papaya, Azadirachta indica, andthe rhizomes of Curcuma longa were plucked from the parent plants, air-dried, and milled into powders.

Collection of substrate

Cow dung, used as substrate was collected freshly from the Livestock Section of the Teaching and Research Farm of the Federal University of Technology, Akure.

Construction of bioreactors

Four bioreactors were constructed using yellow gallons of 25 litre capacity according to the methods of [5]. Three of the gallons contained cow dung mixed with 50 g of each of the plant powders and designated as CDNP (cow dung mixed with neem powders); CDTP (cow dung mixed with tumeric powders), and CDPP (cow dung mixed with papaya powders) while the fourth gallon (CDTRL) served as control (cow dung without plant powders). The original opening of the gallons was used as the slurry inlet and an outlet (gas outlet) was fabricated on each of gallon’s lid using a half inch pipe connected to 6.36mm diameter gas transparent hose using the method of [6] and then connected to a tyre tube of 0.8kg as the gas collector [7]. Each of the connections was made airtight using hose clamps.

Charging of bioreactors

The mixtures were charged into the 25 litre prototype batch bioreactor where the waste mixture was loaded once and sealed for a retention period of 30 days, allowing for anaerobic digestion to occur without further input or output. The waste was charged up to three-quarters of the bioreactor volume, leaving headspace for gas collection. The bioreactor contents were stirred adequately daily to ensure substrate homogeneity.

Determination of Quantity of Biogas Produced

The quantity of biogas produced (kg) was obtained by taking daily measurements of the tubes (gas collector) using a digital measuring scale (CAMMRY Model).

Determination of microbial counts and isolation

One (1) ml of 10^-4 dilutions of the homogenates prepared from the various bioreactors at days 0, 15 and 30 were pipetted aseptically and dispensed into sterilized Petri dishes. This was followed by pouring the already prepared nutrient agar medium into the Petri dishes. The Petri dishes were swirled gently for homogeneity of the contents, then allowed to solidify and later incubated upside down at 37°C for 24 hours. After 24 hours, observed colonies were counted with a colony counter device (Gallenkamp Model, UK) and each count expressed as colony-forming units per millilitre (cfu/ml).

 Identification and characterization of isolates

The observed colony of each bacterial isolate was sub cultured severally until a pure culture was obtained using the method of [8]. The pure colonies were later examined using their cultural and morphological attributes [9]. Additionally, biochemical tests which included Gram staining, catalase, coagulase, sugar fermentation, starch hydrolysis and spore staining assay was carried out to assist in the characterisation and identification of the isolated bacteria.

pH determination

The pHwas determined by inserting the electrode of a digital pH-meter (Jenway 550 model) directly into each digestate / bioreactor sample at days 0, 15, 30 and readings taken thereafter.

Statistical analysis

All the data on biogas yield, microbial populations and pHof digesting materials obtained from this study were subjected to Analysis of Variance (ANOVA) and where significant, the means were compared at 5% level of probability using Duncan’s Multiple Range Test (SPSS Version 20). The results were expressed as mean ± standard deviation.

Results

Total biogas yield from the different bioreactors

            The total biogas yield (kg) from the 4 bioreactors throughout the 30-day retention time is shown in Table 1. At day 0, there was no biogas yield (0.00 ± 0.00) across the cow dung treated with all the different plant powders and cow dung without treatment. However, there was significant increase in the biogas yield in all the bioreactors as observed by day 15 with CDNP having the highest biogas yield (0.38 ± 0.03), followed by CDTP (0.16 ± 0.06), then CDPP (0.09 ± 0.08), while CTRL had the least (0.07 ± 0.03) and the various biogas yields in each bioreactor were significantly different (p<0.05) from one another (Table 1).  Finally, on day 30, CDNP still maintained the highest yield (0.63 ± 0.03), next was CDPP (0.29 ± 0.09), then CDTP (0.25 ± 0.06kg) while CTRL had the least yield (0.17 ± 0.02). Again, the various biogas yield in each bioreactor were significantly different (p<0.05) from one another (Table 1).

Microbial population of digesting materials

The bacteria count (×10⁴ cfu/ml) of each digestate in the different bioreactors is shown in Table 2. At day 0, cow dung without plant powders (CTRL) had the highest bacteria counts (13.02 ± 0.02), followed by cow dung with papaya powders (CDPP) having 12.01 ± 0.23 as their bacteria counts, then cow dung with neem powders (CDNP) having bacteria counts of 11.02 ± 0.03 while the least bacteria counts (10.01 ± 0.05) was observed in cow dung with tumeric powders (CDTP). Notably, the various bacteria counts were significantly different (p<0.05) from one another.

However, as digestion continued till day 15, the reverse was the case. For instance, CDTP had the highest bacteria counts (14.02 ± 0.02), followed by CDNP (13.00 ± 0.01), CDPP (11.01 ± 0.02) while CTRL had the least bacteria counts (9.01 ± 0.01). Specifically, the various bacteria counts were significantly different (p<0.05) from one another (Table 2). Finally, as digestion ended by day 30, CDNP now had the highest bacteria counts (31.02 ± 0.03), followed by CDPP (26.01 ± 0.01), then CDTP (17.01 ± 0.01) and the least count was still noticed in CTRL (13.01 ± 0.02). Notably, the various bacteria counts were significantly different (p<0.05) from one another (Table 2).

Isolation of microorganisms

A total of five (5) bacteria were isolated over 30 days retention time of biogas generation, based on morphological and biochemical characteristics (Table 3). The various bacteria isolates included Bacillus species, Pseudomonas species, Escherichia coli, Lactobacillus species and Methanobacterium species.

Bacteria counts of different isolates from the various bioreactors

The bacteria counts (×10⁴cfu/ml) of different isolates from the bioreactor of Cow Dung with Neem Powders (CDNP) are shown in Table 4. At day 0, It was observed that, Bacillus sp had the highest count (4.02 ± 0.01), followed by Escherichia coli (3.07 ± 0.00), then Pseudomonas sp (2.08 ± 0.00) and Methanobacterium sp (2.04 ± 0.01) while Lactobacillus sp had the lowest counts (1.02 ± 0.0010). Notably, the various bacteria counts were significantly different (p<0.05) from one another, aside Pseudomonas sp and Methanobacterium sp that were significantly not different (p>0.05). However, as digestion continued till day 15, Pseudomonas sp had the highest count (4.00 ± 0.01), then Lactobacillus sp (3.00 ± 0.00), Methanobacterium sp (3.00 ± 0.00) and Bacillus sp (2.00 ± 0.00) while the least counts were observed in Escherichia coli (1.00 ± 0.00).

The various bacteria counts were significantly different (p<0.05) from one another, except Lactobacillus sp and Methanobacterium sp that were significantly not different (p>0.05) (Table 4). Finally, after digestion by day 30, Methanobacterium sp was observed having the highest bacteria counts (15.05 ± 0.01) followed by Bacillus sp (7.05 ± 0.01), then Escherichia coli (6.06 ± 0.01), Pseudomonas sp (2.03 ± 0.00) then Lactobacillus sp had the least bacteria counts (1.04 ± 0.00) and the various bacteria counts were significantly different (p<0.05) from one another (Table 4).

Note: Each value represents a mean of 5 replicates and where significant, the means were separated using Duncan multiple range test (DMRT) at p≤ 0.05. Values with the same alphabet across the rows are not significantly different (p≤ 0.05). CDNP – cow dung mixed with neem powders; CDTP – cow dung mixed with tumeric powders; CDPP – cow dung mixed with papaya powders; CTRL – control (cow dung without any powder)

Note: Each value represents a mean of 5 replicates and where significant, the means were separated using Duncan multiple range test (DMRT) at p≤ 0.05. Values with the same alphabet in the same column are not significantly different (p≤ 0.05). CDNP – cow dung mixed with neem powders; CDTP – cow dung mixed with tumeric powders; CDPP – cow dung mixed with papaya powders; CTRL – control (cow dung without any powder)

The bacterial counts (×10⁴cfu/ml) of different isolates from the bioreactor of cow dung with tumeric powders (CDTP) were reported in Table 5. At day 0, Escherichia coli had the highest counts (4.03 ± 0.01), followed by Bacillus sp (3.03 ± 0.00), then Pseudomonas sp (2.05 ± 0.00), Methanobacterium sp (1.02 ± 0.00) while Lactobacillus sp had the lowest counts (0.00 ± 0.00). Interestingly, the various bacteria counts were significantly different (p<0.05) from one another (Table 5). However, by day 15, Lactobacillus sp had the highest counts (5.04 ± 0.01), followed by Pseudomonas sp (4.04 ± 0.00) and Methanobacterium sp (4.09 ± 0.01), then Escherichia coli (1.00 ± 0.00), while Bacillus sp had the least count (0.00 ± 0.00). The various bacteria counts were significantly different (p<0.05) from one another, except Pseudomonas sp and Methanobacterium sp that are significantly not different (p>0.05) (Table 5). At day 30, when the digestion ended, it was noted that Methanobacterium sp had the highest bacteria counts (12.00±0.01), followed by Bacillus sp (3.01±0.00), then Escherichia coli (2.00 ± 0.00) while Pseudomonas sp and Lactobacillus sp both had least counts (0.00 ± 0.00). Notably, the various bacteria counts were significantly different (p<0.05) from one another, except Pseudomonas sp and Lactobacillus sp that were significantly not different (p>0.05) (Table 5).

Furthermore, the bacterial counts (×10⁴cfu/ml) of different isolates from the bioreactor of cow dung with papaya powders (CDPP) was presented in Table 6.  It was observed at day 0 that Bacillus sp had highest counts (5.00 ± 0.06), then Escherichia coli (3.00 ± 0.04), followed by Pseudomonas sp (2.00 ± 0.07) and the various counts were significantly different (p<0.05) from one another. Meanwhile, Lactobacillus spand Methanobacterium sp had the least bacteria counts (1.00 ± 0.00) that were not significantly different (p>0.05). As the digestion process advanced to day 15, the highest counts were observed in Lactobacillus sp (4.00 ± 0.01), then Pseudomonas sp (3.00 ± 0.00), Methanobacterium sp (3.00 ± 0.01), and Bacillus sp (1.00 ± 0.00) while Escherichia coli had the least counts (16.00 ± 0.00). Notably, the various bacteria counts were significantly different (p<0.05) from one another, except Pseudomonas sp and Methanobacterium sp that were significantly not different (p>0.05) (Table 6).

After the digestion by day 30, it was noted that Methanobacterium sp had the highest counts (16.00 ± 0.00), next was Bacillus sp (7.00 ± 0.00), then Escherichia coli (3.00±0.00), while Pseudomonas sp (0.00 ± 0.00) and Lactobacillus sp (0.00 ± 0.00) had the least count. The various bacteria counts were significantly different (p<0.05) from one another, aside Pseudomonas sp and Lactobacillus sp which were significantly not different (p>0.05) (Table 6).

The bacterial counts (×10⁴cfu/ml) of different isolates from the bioreactor of cow dung without plant powders (CTRL) are shown in Table 7. At day 0, Bacillus sp had the highest counts (6.00 ± 0.00), followed by Escherichia coli (4.00 ± 0.00) then Pseudomonas sp (2.00 ± 0.00), followed by Methanobacterium sp while Lactobacillus sp had the least counts (0.00 ± 0.00) and the various bacteria counts were significantly different (p<0.05) from one another.

At day 15, Lactobacillus sp was observed to have the highest counts (4.00 ± 0.00), followed by Pseudomonas sp (3.00 ± 0.0032), then Methanobacterium sp (2.00 ± 0.00), while Bacillus sp and Escherichia coli had the lowest counts (0.00 ± 0.00). The various bacteria counts were significantly different (p<0.05) from one another, other than Bacillus sp and Escherichia coli which are significantly not different (p>0.05) (Table 7).

However, at day 30, Methanobacterium sp had the highest counts (8.00 ± 0.01) then Bacillus sp (4.00 ± 0.01) and Escherichia coli (1.00 ± 0.00), while Lactobacillus sp and Pseudomonas sp were observed to have the least bacteria counts (0.00 ± 0.00) and the various

Note: Each value represents a mean of 5 replicates and where significant, the means were separated using Duncan multiple range test (DMRT) at p≤ 0.05. Values with the same alphabet in the same column are not significantly different (p≤ 0.05).

Note: Each value represents a mean of 5 replicates and where significant, the means were separated using Duncan multiple range test (DMRT) at p≤ 0.05. Values with the same alphabet in the same column are not significantly different (p≤ 0.05).

Note: Each value represents a mean of 5 replicates and where significant, the means were separated using Duncan multiple range test (DMRT) at p≤ 0.05. Values with the same alphabet in the same column are not significantly different (p≤ 0.05).

Note: Each value represents a mean of 5 replicates and where significant, the means were separated using Duncan multiple range test (DMRT) at p≤ 0.05. Values with the same alphabet in the same column are not significantly different (p≤ 0.05). CDNP – cow dung mixed with neem powders; CDTP – cow dung mixed with turmeric powders; CDPP – cow dung mixed with papaya powders; CTRL – control (cow dung without any powder)

bacteria counts were significantly different (p<0.05) from one another, aside Lactobacillus sp and Pseudomonas sp that are significantly not different (p>0.05) (Table 7).

pH values of the different bioreactors

 pH change of each digestate within 30 days retention time was shown in Table 8. At day 0, CDPP had highest pH value (7.20 ± 0.01), followed by CDTP (7.18 ± 0.02), then CTRL (6.90 ± 0.02) while CDNP was observed to have the least pH value (6.80 ± 0.06). Interestingly, various pH values were significantly different (p<0.05) from one another. However, there was a general decrease in the pH values in all the bioreactors at day 15, though CDTP now had the highest value (6.00 ± 0.00), followed by CDPP (5.89 ± 0.02), then CTRL (5.63 ± 0.01) while CDNP had the least value (5.21 ± 0.01) and the various pH values were significantly different (p<0.05) from one another. Finally at day 30, the pH values increased notably in all bioreactors again from CDNP (7.35 ± 0.01), then CDPP (7.15 ± 0.01) to CDTP (7.09 ± 0.01) while CTRL had the least pH value (6.95 ± 0.01) and various pH values were significantly different (p<0.05) from one another (Table 8).

Discussion

The study showed the influence of the various plant powders in enhancing biogas production because all the different bioreactors (CDNP, CDTP and CDPP) yielded more biogas when compared with the untreated cow dung (CTRL) over 30 days retention time. This is supported by the works [10] who reported that plant extracts are said to maintain favourable conditions for enhanced biogas production in anaerobic digestion by enhancing enzymes that extensively degrade lignin and break down cellulose and hemicellulose. Among all the different bioreactors, CDNP had the highest biogas weight. This may not be unconnected with the activity of azadirachtin, a bioactive compound present in neem, ultimately boosting biogas yield [11-12]. The swift hydrolysis and acidogenesis may be enhanced by the stronger antimicrobial effects of azadirachtin present in neem, which enriched the growth of methanogens.

Again, it has been reported in literatures [13-14] that anaerobic digestion relies on the combined efforts of hydrolytic, acidogenic, acetogenic, and methanogenic bacteria to break down organic matter. Undoubtedly, findings from this study showed that CDNP, CDTP and CDPP digester systems had more bacterial loads when compared with CTRL. Remarkably, CDNP had the highest bacterial loads. The notable increase in microbial counts at day 30 of digestion suggests adaptation and proliferation of resilient microbes, contributing to enhanced biogas production especially in CDNP [15]. This finding is consistent with [16], who noted that anaerobic digestion involves a sequence of bacterial groups, including hydrolyzing, acidifying, acetogenic, and methanogenic bacteria.

A total of five bacterial species were isolated from the anaerobic digestion of the cow dung and the identified isolates included species of Bacillus, Pseudomonas, Methanobacterium, Lactobacillus and Escherichia coli. This was supported by the findings of [17] who confirmed the presence of hydrolytic bacteria such as Bacillus spp., Pseudomonas spp.; acidogenic bacteria such as Escherichia coli, Lactobacillus spp.; acetogenic bacteria such as Lactobacillus sp and methanogenic bacteria such as Methanobacterium sp from microbial community composition in staged anaerobic digesters fed with food waste. This is also in accordance with [18] who reported that bacteria are responsible for anaerobic digestion in the production process of biogas. In facts, research conducted by [19] on biogas production from co-digestion of cow dung and chicken droppings isolated Bacillus, Pseudomonas spp, Serratia, Enterococcus, and Klebsiella as cellulose and amylase producing bacteria while [20] equally identified Bacillus spp, Pseudomonas spp, Micrococcus spp, and Proteus spp in their own work. Meanwhile the presence and identification of Methanobacterium spp in this study is equally buttressed by the works of [21] who isolated and identified Methanobacterium spp from chicken droppings while [22] also isolated and identified Methanobacterium spp from human wastes.

Meanwhile, the presence and dominance of Bacillus spp in all the four bioreactors (CDNP, CDTP, CDPP, and CTRL) before digestion (day 0) might not be unconnected with their ability to develop resistant spores, enabling survival in oxygen-rich environments and tolerance to transitions between aerobic and anaerobic conditions [23]. They also produce extracellular hydrolytic enzymes that facilitate the breakdown of polymer compounds through hydrolysis [24]. Furthermore, Lactobacillus spp. dominated CDTP, CDPP, and CTRL digester systems by day 15. These bacteria excel at lactic acid fermentation, breaking down simple sugars mainly into lactic acid, and sometimes producing acetate, ethanol, and carbon dioxide. Their metabolic activity acidifies the environment, which is significant because methanogenic bacteria are sensitive to pH. This acidification by Lactobacillus spp. influences the emergence of methanogens [25].

In contrast, Pseudomonas spp only occurred in CDNP at day 15. Pseudomonas species are versatile, utilizing various carbon sources, degrading aromatic compounds, forming biofilms, and exhibiting anaerobic respiration using nitrate. Their dominance may be attributed to specific substrate or nutrient conditions, as well as tolerance to inhibitory compounds [26].

At day 30, Methanobacterium spp. had the highest loads in CDNP, CDTP, CDPP, and CTRL digester systems. This shows they were dominant in the methanogenesis phase of anaerobic digestion. [23] reported that Methanobacterium spp. can thrive in different environmental conditions, allowing them dominate in various digester setups. Their growth by day 30 marked the methanogenesis stage where these microbes use hydrogen and carbon dioxide to make methane, the key gas for biogas production. Their dominance by day 30 cannot help but be connected with the fact that methanogens are extremely sensitive to conditions like pH, shaping digester microbiome dynamics.

For pH readings, the values varied throughout the retention period with the pH values of CDNP, CDTP and CDPP considerably higher when compared with the CTRL. This is in accordance with the work of [27] who reported that anaerobic conditions caused organic matter to ferment, producing resultant products with varying pH values in a biogas digester [28]. The initial acidic pH levels can be attributed to the activity of aerobes and facultative anaerobes producing acidic metabolites, a crucial step for methane production. No wonder, there was decreased pH on 15th day of the digestion process and perhaps the reason why acid-tolerant organisms like Bacillus and Lactobacillus predominated in the digestion process by day 15. The observed pH decline can be linked to metabolite accumulation, including acetate, hydrogen gas, carbon dioxide, and volatile fatty acids [29].

However, on the day 30 of the retention duration, the pH increased due to methanogenesis which is the fourth stage of biogas production and methanogenesis stage typically requires a neutral- alkaline pH environment. Hence, these pH fluctuations, according to [30] are normal in biogas systems and can serve as an indicator of biogas production status. In the same vein, the observed pH is consistent with earlier research by [31], who reported that methanogens thrive best within a pH range of 5 to 8 during biogas production and the same case was also noticed in this work during methanogenesis.  Even an optimal pH range of 5 to 8 for biogas production has been reported [32]. Likewise, [33] and [34] also observed that methanogenesis thrives within a pH range of 5-7.8. Hence, effective pH management is essential for optimizing anaerobic digestion efficiency and advancing biogas production.

             Conclusions

This study demonstrates the potential of cow dung catalyzed by plant powders as a valuable resource for biogas production over 30 days retention time. The selected plant powders positively influenced the production of biogas with neem powder being the most effective for optimising biogas yield than other powders, as the cow dung treated with neem plant powders had the highest biogas weight. Thus, neem powders being natural, affordable and eco-friendly could be explored and utilized by biogas operators in farm waste digestion.

Authors’ Contributions

Conceptualisation: OO and JA; Investigation: OO, OJ and JA; Supervision: OO and OJ; Visualisation:  OO, OJ and JA; Writing original draft: OO; Writing-review and editing: OO, OJ and JA. All authors read and approved the final manuscript.          

Acknowledgements

 This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of Interests

The authors declare that there are no conflicts of interest related to this article.

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