Kinetic Study on Flooded Soil Recovery Using Soil Containing Arbuscular Mycorrhizal Fungi

Zainol, Norazwina; Thangaperumal, N.; Zahari, N.H.; Aziz, Nor Hazwani

doi:10.1051/matecconf/20167801087

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…In addition, a diverse soil microbial community can provide nutrients to crops (Jacoby, Peukert, Succurro, Koprivova, & Kopriva, 2017). Specifically, the symbiotic relationship between AMF with plant roots aids in the uptake of water and nutrients (Berruti et al, 2014), and plant P deficiency can occur due to reduced populations of AMF, even when adequate soil P concentrations are measured (Zainol, Thangaperumal, Zahari, & Aziz, 2016).…”

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confidence: 99%

A comparison between fatty acid methyl ester profiling methods (PLFA and EL‐FAME) as soil health indicators

Cano

Acosta-Martínez

et al. 2020

Soil Science Soc of Amer J

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Fatty acid methyl ester (FAME) profiling for characterizing microbial community composition typically is conducted via phospholipid fatty acid (PLFA) or ester‐linked fatty acid methyl ester (EL‐FAME) methods. As soil health assessments aim to be utilized across the nation and globe, the robust measurement and interpretation of microbial communities across a range of soils and environments will be necessary. This study compared PLFA and EL‐FAME methods for detecting and interpreting profiles of microbial community composition in croplands across a wide geographic area using a total of 172 soil samples from 14 states representing a wide range of soil properties. Overall, PLFA and EL‐FAME provided comparable biomarkers in terms of microbial community composition. The Spearman's Rank correlation test showed positive correlations (r = 0.37–0.71) between PLFA and EL‐FAME methods for absolute abundance of total FAME and individual microbial groups including fungi (saprophytic fungi [SF], arbuscular mycorrhizal fungi [AMF], and general fungi [F]) and all bacterial groups (Gram positive [GMP], Gram negative [GMN], and Actinobacteria). In both methods, a common set of fatty acids were influential in differentiating samples. The main differences in biomarker abundances between the two methods were that fungal and Actinobacteria biomarkers (e.g., 16:1ω5c [AMF], 18:1ω9c [F], 18:3ω6c [F], and 10Me16:0 [Actinobacteria]) were more abundant or critical in EL‐FAME profiling (large variation among soil samples and sensitive to soil properties), but bacterial biomarkers such as i15:0 (GMP), 16:1ω7c (GMN), 18:1ω7c (GMN), and cy19:0ω7c (GMN) were more dominant and responsive to soil properties in PLFA profiling. The practical advantages of EL‐FAME are lower cost and simpler methodology. Although both methods produced similar microbial profile abundances for important microbial markers, PLFA was more sensitive to the wide range of soil chemical properties in this sample set including pH, clay content, soil organic matter, and active carbon.

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confidence: 99%

A comparison between fatty acid methyl ester profiling methods (PLFA and EL‐FAME) as soil health indicators

Cano

Acosta-Martínez

et al. 2020

Soil Science Soc of Amer J

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“…All data obtained were recorded in Design Expert software. The responses were analyzed using ANOVA based on p-value with 95% of confidence level to identify the most contributing factors and interaction between the factors that has an effect on N [7].…”

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confidence: 99%

Application of Artificial Neural Network to Improve Pleurotus sp. Cultivation Modelling

2019

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Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Both substrates were collected and prepared as the selected factors which were type of substrate (SB - A and EFB - B), size of substrates (0.5 cm and 2.5 cm), mass ratio of spawn to substrate (SP/SS) (1:10 and 1:14), temperature during spawn running (25°C and ambient) and pre-treatment of substrates (steam and non-steam). The response was nitrogen concentration in mycelium (N). This paper presents the application of artificial neural network to improve the modelling process. Artificial neural network is one of the machine learning method which use the cultivation process information and extract the pattern from the data. Neural network ability to learn pattern by changing the connection weight had produced a trained network which represent the Pleurotus sp. cultivation process. Next this trained network was validated using error measurement to determine the modelling accuracy. The results show that the artificial neural network modelling produced better results with higher accuracy and lower error when compared to the mathematical modelling.

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Kinetic Study on Flooded Soil Recovery Using Soil Containing Arbuscular Mycorrhizal Fungi

Cited by 2 publications

References 13 publications

A comparison between fatty acid methyl ester profiling methods (PLFA and EL‐FAME) as soil health indicators

A comparison between fatty acid methyl ester profiling methods (PLFA and EL‐FAME) as soil health indicators

Application of Artificial Neural Network to Improve Pleurotus sp. Cultivation Modelling

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