Synergistic mitigation of nitrogen loss and gaseous emissions in field-scale chicken manure composting using light calcium carbonate loaded with microbial inoculants
Description
This dataset contains experimental data from a 34-day field-scale chicken manure composting study. The experiment included three treatments: light calcium carbonate (LCC) amendment alone, LCC combined with commercial microbial inoculant (CMI), and LCC combined with self-designed microbial inoculant (SDMI). The dataset includes dynamic changes in compost physicochemical properties (temperature, pH, electrical conductivity, moisture content, ammonium nitrogen, nitrate nitrogen, and total nitrogen), cumulative emissions of NH₃, N₂O, CO₂, and CH₄, as well as the relative abundance of nitrogen-cycling functional genes .
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Steps to reproduce
A 34-day field-scale aerobic composting experiment was performed using chicken manure and cassava residue (5:2, w/w) as raw materials. Three treatments were established, including light calcium carbonate (LCC) alone, LCC combined with commercial microbial inoculant (CMI), and LCC combined with self-designed microbial inoculant (SDMI). The commercial inoculant consisted of functional strains such as Bacillus subtilis, whereas the self-designed inoculant was screened from mature chicken manure compost with high capacities for nitrogen transformation and organic matter degradation. Both inoculants were loaded onto LCC to prepare solid additives and uniformly incorporated into the composting substrates at 5% of the initial wet weight. Manual turning was conducted daily throughout the composting process to maintain aerobic conditions. Pile temperature and ambient temperature were recorded daily. Gas samples were collected at scheduled intervals using the static chamber method. Ammonia (NH₃) concentration was determined via the indophenol blue colorimetric method, while the concentrations of nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄) were measured using an Agilent 7890B gas chromatograph. Instantaneous gas fluxes and cumulative emissions were calculated according to standard empirical formulas reported in previous studies. Solid compost samples were collected at key composting stages using the three-point sampling method for the determination of moisture content, pH, electrical conductivity (EC), ammonium nitrogen, nitrate nitrogen, and seed germination index (GI). The moisture content was measured by the constant-weight drying method. The pH and EC values were determined using a 1:10 (w/w) water extraction method. Inorganic nitrogen contents were analyzed via ultraviolet spectrophotometry after potassium chloride extraction. Samples for physicochemical analysis were stored at 4 °C and measured within 48 h, while samples for molecular analysis were preserved at −70 °C. Total genomic DNA was extracted from compost samples using the HiPure Soil DNA Mini Kit. The abundances of nitrogen-cycling functional genes, including AOA-amoA, AOB-amoA, nirS, and nosZⅠ, were quantified using a CFX96 real-time quantitative PCR (qPCR) system. The absolute gene copy numbers were calculated based on standard curves, and the relative gene abundances were obtained by normalization against bacterial 16S rRNA gene copy numbers. Raw experimental data were sorted using Excel. One-way analysis of variance (ANOVA) and least significant difference (LSD) multiple comparisons were performed with SPSS 27.0. Correlation analysis, random forest analysis, and partial least squares structural equation modeling were conducted using Python and R. Data visualization was completed with GraphPad Prism 9.5.0 to systematically explore the effects of different treatments on nitrogen transformation and greenhouse gas emissions during chicken manure composting.
Institutions
- Nanjing University of Information Science and TechnologyJiangsu, Nanjing