Quantity changes in Pseudomonas species in dairy manure during anaerobic digestion at mesophilic and thermophilic temperatures

Iwasaki, Masahiro; Qi, Guangdou; Endo, Yumiko; Pan, Zhifei; Yamashiro, Takaki; Andriamanohiarisoamanana, Fetra J.; Ihara, Ikko

doi:10.1007/s10163-018-0800-z

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…On this basis, for the construction of the urban sprawl measurement system in the Yangtze River Economic Belt, the measurement standard and measurement index are mainly considered. In terms of the measurement standard constructed for the measurement system of cities in the Yangtze River Economic Belt, the major consideration of the quantitative growth measurement is the phenomenon of increasing the construction area caused by the disorderly expansion of the city [35,36]. Discontinuous measurement, in fact, is a representation of urban sprawl disorder in urban form.…”

Section: Construction Of Urban Sprawl Measurement Systemmentioning

confidence: 99%

The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

Huafang

Cheng

2020

IJERPH

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In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban sprawl is explained. According to the actual development situation of cities in the Yangtze River Economic Belt, smart growth theory is selected as the basic regulation method of urban sprawl. Second, the back propagation neural network (BPNN) algorithm under deep supervised learning is applied to construct a smart evaluation model of land use growth. Finally, based on the actual development of cities in the Yangtze River Economic Belt, the quantitative growth measurement method is selected to construct a measurement system of urban sprawl in the Yangtze River Economic Belt, and the empirical analysis is carried out. The training results show that the proposed BPNN smart growth evaluation model, based on deep supervised learning, has good evaluation accuracy, and the error is within the preset range. The analysis of the quantitative growth-based measurement system in the increase of urban construction land shows that the increase in urban construction land area of the Yangtze River Economic Belt from 2014 to 2019 was 78.67 km2. Meanwhile, the increases in urban construction land area in different years are different. The empirical results show that the population composition of the Yangtze River Economic Belt and the urban construction area between 2005 and 2019 show a trend of increasing annually; at the same time, urban sprawl development shows a staged characteristic. It is of great significance to apply deep learning fusion neural network algorithm in the construction of the urban sprawl measurement system, which provides a quantitative basis for the in-depth analysis and discussion of urban sprawl.

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Section: Construction Of Urban Sprawl Measurement Systemmentioning

confidence: 99%

The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

Huafang

Cheng

2020

IJERPH

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“…Hence codigestion improved and enriched the biofertilizer properties of the resultant digestion product. This increase in populations of PGPB could be attributed to the use of goat manure as co-substrate and studies have shown that anaerobic digestate can serve as sources of biofertilizing bacteria through the addition of readily available nutrients to soil [21,34,41].…”

Section: Plant Growth Promoting Bacterial Populations In Anaerobic DImentioning

confidence: 99%

Enhanced Biogas and Biofertilizer Production from Anaerobic Codigestion of Harvest Residues and Goat Manure

Ndubuisi-Nnaji

Ofon

Asamudo

et al. 2020

JSRR

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Comparative assays were conducted to assess the biogas and biofertilizer production efficiency from anaerobically codigested goat manure (GM) and harvest residues: corn stover (CS) and rice straw (RS). All digesters were operated simultaneously under mesophilic temperature of 40°C and notable phosphate solubilizing and nitrogen fixing bacterial populations indicated qualitative biofertilizer quality of the digestates. Codigestion of the substrates significantly increased biogas yield (p < 0.05) compared to monodigestion, and the highest cumulative yield of 573 ml/g VS was obtained from codigested rice straw (RS) and goat manure (GM). With a significant decimation in number of pathogens (p < 0.05), a 2 – 3 fold increase in populations of plant growth promoting bacteria (Bacillus and Pseudomonas species) was observed in digestate from codigestion assays when compared to monodigestion (control) and were identified as Clostridium sp., Bacillus subtilis, Bacillus megaterium, Lactobacillus sp., Pseudomonas fluorescens including methanogens: Methanothrix sp., Methanobacterium sp. and Methanosarcina sp. On the average, codigestion assays resulted in enhanced biogas yield and biofertilizer quality that was 2.3 – 4.1 times higher than single substrate digestion and generally improved the efficiency of biogas and biofertilizer production.

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“…The selection process resulted in significant disparities between the rhizosphere and bulk soil’s species diversity and microbial interaction networks . Additionally, FWD is a rich source of a variety of microorganisms that can be beneficial to agriculture or have an environmental impact such as nitrogen-fixing bacteria such as Bradyrhizobium , denitrifying and nitrifying bacteria such as Achromobacter denitrificans and Thiobacillus denitrificans and Nitrosomonas , soil methanogens such as Methanosaeta and Methanosarcina , plant growth-promoting bacteria such as Bacillus and Pseudomonas , Actinomyces , saprophytic fungi, and arbuscular mycorrhizal fungi such as Glomeromycota . − It is essential to understand how the bulk soil microbiome differs from the rhizosphere microbiome to regulate the rhizosphere microbiome and ensure sustainable ecosystem functioning. In this work, extensive adoption of a pilot scale AD system for FW treatment is conducted on-site, at a representative hawker centre in Singapore .…”

Section: Introductionmentioning

confidence: 99%

Plant Growth Enhancement under Combined Amendments of Food Waste Digestate and Rhizosphere Microbial Co-Occurrence

Sharma,

Tiong,

Yan

et al. 2024

ACS Sustainable Resour. Manage.

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Food waste contributes to environmental degradation by emitting greenhouse gases and depleting natural resources, but it also exacerbates global hunger and poverty. This study investigated the use of food waste digestate (FWD) as fertilizer on ornamental plant (Piper sarmentosum) cultivation under soil amendment growing media such as organic compost (OC), biochar, and rice husk (RH) in a 56-day trial. A significant increase in plant growth was observed in the potting mix with soil amendments combined with the application of FWD in comparison to the potting mix without the application. In comparison to soil amendments such as OC, FWD found a higher chlorophyll content index of 1.48%. In comparison to biochar, this was followed by 2.76% higher and 1.29% higher than RH. Furthermore, the number of leaves and leaf areas of plants increased significantly (>2%) with FWD added to soil amendments in comparison with OC and RH. The identified microbial phyla were Acidobacteriota, Actinobacteriota, Bacteroidota, Firmicutes, Proteobacteria, and Verrucomicrobiota, and some have closed to networking, like Firmicutes and Proteobacteria. Proteobacteria, Firmicutes, and Bacteroidetes were listed as plant growth-promoting microbe groups in the study. This approach promotes plant growth and offers a sustainable solution to recycling FW into fertilizer.

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Quantity changes in Pseudomonas species in dairy manure during anaerobic digestion at mesophilic and thermophilic temperatures

Cited by 10 publications

References 37 publications

The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

Enhanced Biogas and Biofertilizer Production from Anaerobic Codigestion of Harvest Residues and Goat Manure

Plant Growth Enhancement under Combined Amendments of Food Waste Digestate and Rhizosphere Microbial Co-Occurrence

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