Elsevier

Science of The Total Environment

Volume 586, 15 May 2017, Pages 1003-1011
Science of The Total Environment

Characterization of lignocellulosic compositions' degradation during chicken manure composting with added biochar by phospholipid fatty acid (PLFA) and correlation analysis

https://doi.org/10.1016/j.scitotenv.2017.02.081Get rights and content

Highlights

  • PLFA reflects microbial community succession and lignocellulose degradation well.

  • PLFA helps mechanism exploration for chicken manure composting with added biochar.

  • Good correlation is found between lignocellulose degradation ratio and PLFA.

  • Models based on PLFA18:2ω6c–18:3ω3 can estimate lignocellulose degradation.

Abstract

Biorefractory high polymer lignocellulosic compositions may limit rapid composting and stable decomposition. Because their degradation during composting is not well understood, the correlation with microbial community profiles was assessed to reveal degradation mechanism of lignocellulosic compositions. Testing of chicken manure aerobic composting with added biochar was performed using phospholipid fatty acid (PLFA) and correlation analysis. Results demonstrated a good composting effect with good dynamic correlation between microbial characteristic (PLFA) and lignocellulosic compositions' degradation ratio. The prediction model for hemicellulose degradation ratio (R2 = 0.97, SEP = 3.24) and the prediction model for cellulose degradation ratio (R3 = 0.94, SEP = 3.09), built using PLFA 16:0–18:2ω6c and PLFA 18:2ω6c–18:3ω3 as the arguments had good predictive ability. Based on microbial analysis and quantitative characterization of the degradation ratio, the prediction models provided methodological support for delineating the mechanism of lignocellulosic compositions' degradation during chicken manure aerobic composting with added biochar.

Introduction

With the rapid development of a large-scale livestock industry in China, large quantities of improperly treated manure have caused serious environmental pollution, resulting in a huge waste of organic fertilizer resources (Li et al., 2016, Rashad et al., 2010). In recent years, microbial-based aerobic composting has become an important means to efficiently degrade organic waste, reduce and detoxify waste stockpiles, and however, it maximizes resource utilization efficiency (Chen et al., 2014, Jurado et al., 2015). Therefore lignocellulose is an important raw compost component, and meanwhile it is also a main factor that limits rapid composting and restricts humus formation (Zhang et al., 2014a, Zhang et al., 2014b, Jurado et al., 2015, Zhang et al., 2015). Therefore, elucidation of the process of lignocellulose degradation during composting has important practical significance in interpreting reaction mechanism, evaluating compost maturity and optimizing the composting process. Concurrently, it is also important to research the microorganisms, a core element contributing to the success of rapid composting (Herrmann and Shann, 1997). At present, the separation culture method is the most common way for microbial profiling analysis. However, microbes identified using this method amount to less than 10% of the total environmental microbial constituents; therefore, this method does not reflect the actual microbial profile distribution within the compost environment (Kato et al., 2005, Bossio et al., 1998, Zelles et al., 1995). Because the phospholipid fatty acid (PLFA) spectrum analysis method does not require culturing of organisms, it holds promise as a better method for unbiased microbial community profiling. This method exploits the fact that phospholipids are an important part of living cell membranes and their content is predictably constant for a particular organism. Although various microbes contain distinct types and quantities of PLFAs, they decompose quickly after cell death. Therefore, the PLFA method is a rapid, reliable analytical method to research the real-time dynamic succession of living microbial biomass and community structure (Frostegård and Bååth, 1996, Yu et al., 2009). Recently, the PLFA method has been widely applied to the studies of microbial communities in soil, lake deposits, compost and other environments (Francisco et al., 2016, White et al., 1979, Klamer and Baath, 1998, Lei and VanderGheynst, 2000, VanderGheynst and Lei, 2003, Amir et al., 2008, Daquiado et al., 2013). However, systematic PLFA analysis studies coupling correlation analysis and the quantitative characterization of lignocellulose degradation and microbial community succession are needed to understand degradation mechanisms during aerobic composting.

Previous studies demonstrated that appropriate exogenous additives, such as biochar, could effectively solve the traditional composting problems: slow degradation of organic matter, serious nitrogen element loss and large quantities of greenhouse gas emissions (Steiner et al., 2010, Dias et al., 2010, Jindo et al., 2012a, Jindo et al., 2012b). Biochar is characterized by its high stability, developed pore structure, rich surface functional groups, among other attributes. Such materials are of important practical significance to achieving soil improvement, environmental pollution control and agricultural production (Wang et al., 2016b, Wang et al., 2016a, Trigo et al., 2016, Zhang et al., 2016, Agegnehu et al., 2016, Iqbal et al., 2015, Ulyett et al., 2014, Beesley et al., 2010, Beesley et al., 2011, Zhang et al., 2013) and have become a highlight of composting studies in recent years (Wang et al., 2013, Jia et al., 2016, Cui et al., 2016). However, PLFA-based studies on degradation characteristics of lignocellulosic compositions during chicken manure aerobic composting with added biochar have been rarely reported.

In this study, chicken manure aerobic composting with added biochar was studied for the first time using PLFA analysis to explore dynamic succession changes in relative lignocellulose proportions and microbial community profiles in the aerobic composting process. The quantity of biochar addition was based on the conclusion of previous controlled trials with different additive amount of biochar, which 10% was selected as the optimum adding proportion (Wang et al., 2016b, Wang et al., 2016a). The analysis of correlation between the degradation ratio of lignocellulosic compositions and PLFA microbial profile characteristics, followed by regression modeling analysis throughout the composting process, have facilitated elucidation of the lignocellulosic compositions' degradation mechanism during aerobic composting.

Section snippets

Composting materials

Fresh chicken manure (weight of about 82 kg) from the chicken farm of Shangzhuang Experimental Station of China Agricultural University and wheat straw (weight of about 5 kg) with a length of 3–5 cm obtained from the suburban districts of Beijing were used as the main raw materials for the experiment. These components were fully mixed with about 8.7 kg bamboo biochar (particle size of 3–5 cm), which was produced using high temperature pyrolysis (400– 1000 °C) under oxygen-free conditions, purchased

Temperature

As shown in Fig. 2, the temperature change trend of upper, middle and lower layers of the compost pile during the composting process basically remained consistent, exhibiting divisions (Huang et al., 2010) mainly between mesophilic (0–2 d), thermophilic (3–7 d) and maturity stage (8–27 d). The temperature of the compost pile continuously and rapidly rose and reached the highest temperature of 56.8 °C on the sixth day in the mesophilic and thermophilic stages, presumably because heat was released

Conclusion

Chicken manure aerobic composting with added biochar had good performance for substrate degradation, and biochar could promote OM degradation, microbial reproduction, compost maturation. Moreover, the degradation ratio of hemicellulose and cellulose rose, exceeding lignin ratio. PLFAs could truly reflect microbial community succession. A good dynamic correlation between lignocellulose degradation ratio and PLFA reflected microbial synergy and lignocellulose degradation. Regression analysis

Acknowledgements

This work was supported by the H2020-WASTE-2014-2015 (690142), National Key Research and Development Program of China (2016YFE0112800) and National Key Technology Support Program (2015BAC02B02-02).

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