Improving prediction of N2O emissions during composting using model-agnostic meta-learning.

Nitrous oxide (N2O) represents a significant environmental challenge as a harmful, long-lived greenhouse gas that contributes to the depletion of stratospheric ozone and exacerbates global anthropogenic greenhouse warming. Composting is considered a promising and economically feasible strategy for the treatment of organic waste. However, recent research indicates that composting is a source of N2O, contributing to atmospheric pollution and greenhouse effect. Consequently, there is a need for the development of effective, cost-efficient methodologies to quantify N2O emissions accurately. In this study, we employed the model-agnostic meta-learning (MAML) method to improve the performance of N2O emissions prediction during manure composting. The highest R2 and lowest root mean squared error (RMSE) values achieved were 0.939 and 18.42 mg d-1, respectively. Five machine learning methods including the backpropagation neural network, extreme learning machine, integrated machine learning method based on ELM and random forest, gradient boosting decision tree, and extreme gradient boosting were adopted for comparison to further demonstrate the effectiveness of the MAML prediction model. Feature analysis showed that moisture content of structure material and ammonium concentration during composting process were the two most significant features affecting N2O emissions. This study serves as proof of the application of MAML during N2O emissions prediction, further giving new insights into the effects of manure material properties and composting process data on N2O emissions. This approach helps determining the strategies for mitigating N2O emissions.

Investigation of underlying links between nitrogen transformation and microorganisms' network modularity in the novel static aerobic composting of dairy manure by "stepwise verification interaction analysis".

Conventional composting is a viable method treating agricultural solid waste, and microorganisms and nitrogen transformation are the two major components of this proces. Unfortunately, conventional composting is time-consuming and laborious, and limited efforts have been made to mitigate these problems. Herein, a novel static aerobic composting technology (NSACT) was developed and employed for the composting of cow manure and rice straw mixtures. During the composting process, physicochemical parameters were analyzed to evaluate the quality of compost products, and microbial abundance dynamics were determined using high-throughput sequencing technique. The results showed that NSACT achieved compost maturity within 17 days as the thermophilic stage (≥55 °C) lasted for 11 days. GI, pH, and C/N were 98.71 %, 8.38, and 19.67 in the top layer, 92.32 %, 8.24, and 22.38 in the middle layer, 102.08 %, 8.33, and 19.95 in the bottom layer. These observations indicate compost products maturated and met the requirements of current legislation. Compared with fungi, bacterial communities dominated NSACT composting system. Based on the stepwise verification interaction analysis (SVIA), the novel combination utilization of multiple statistical analyses (Spearman, RDA/CCA, Network modularity, and Path analyses), bacterial genera Norank Anaerolineaceae (-0.9279*), norank Gemmatimonadetes (1.1959*), norank Acidobacteria (0.6137**) and unclassified Proteobacteria (-0.7998*), and fungi genera Myriococcum thermophilum (-0.0445), unclassified Sordariales (-0.0828*), unclassified Lasiosphaeriaceae (-0.4174**), and Coprinopsis calospora (-0.3453*) were the identified key microbial taxa affecting NH4+-N, NO3--N, TKN and C/N transformation in the NSACT composting matrix respectively. This work revealed that NSACT successfully managed cow manure-rice straw wastes and significantly shorten the composting period. Interestingly, most microorganisms observed in this composting matrix acted in a synergistic manner, promoting nitrogen transformation.

Deciphering biochar compost co-application impact on microbial communities mediating carbon and nitrogen transformation across different stages of corn development.

Under current climatic conditions, developing eco-friendly and climate-smart fertilizers has become increasingly important.The co-application of biochar and compost on agricultural soils has received considerable attention recently.Unfortunately, little is known about its effects on specific microbial taxa involved in carbon and nitrogen transformation in the soil.Herein, we report the efficacy of applying biochar-based amendments on soil physicochemical indices, enzymatic activity, functional genes, bacterial community, and their network patterns in corn rhizosphere at seedling (SS), flowering (FS), and maturity (MS) stages.The applied treatments were: compost alone (COM), biochar alone (BIOC), composted biochar (CMB), fortified compost (CMWB), and the control (no fertilizer (CNTRL).The non-metric multidimensional scaling (NMDS) indicated total nitrogen (TN), pH, NO3--N, urease, protease, and microbial biomass C (MBC) as the dominant environmental factors driving soil bacteria in this study.The dominant N mediating genes belonged to nitrate reductase (narG) and nitronate monooxygenase (amo), while beta-galactosidase, catalase, and alpha-amylase were the dominant genes observed relating to C cycling.Interestingly, the abundance of these genes was higher in COM, CMWB, and CMB compared with the CNTRL and BIOC treatments.The bacteria network properties of CWMB and CMB indicated robust niche overlap associated with high cross-feeding between bacterial communities compared to other treatments.Path and stepwise regression analyses revealed norank_Reyranellaceae and Sphingopyxis in CMWB as the major bacterial genera and the major predictive indices mediating soil organic C (SOC), NH4+-N, NO3--N, and TN transformation.Overall, biochar with compost amendments improved soil nutrient conditions, regulated the composition of the bacterial community, and benefited C/N cycling in the soil ecosystem.

Exploration of ß-glucosidase-producing microorganisms community structure and key communities driving cellulose degradation during composting of pure corn straw by multi-interaction analysis.

Poor management of crop residues leads to environmental pollution and composting is a sustainable practice for addressing the challenge. However, knowledge about composting with pure crop straw is still limited, which is a novel and feasible composting strategy. In this study, pure corn straw was in-situ composted for better management. Community structure of ß-glucosidase-producing microorganisms during composting was deciphered using high-throughput sequencing. Results showed that the compost was mature with organic matter content of 37.83% and pH value of 7.36 and pure corn straw could be composted successfully. Cooling phase was major period for cellulose degradation with the highest ß-glucosidase activity (476.25 µmol·p-Nitr/kg·dw·min) and microbial diversity (Shannon index, 3.63; Chao1 index, 500.81). Significant compositional succession was observed in the functional communities during composting with Streptomyces (14.32%), Trichoderma (13.85%) and Agromyces (11.68%) as dominant genera. ß-Glucosidase-producing bacteria and fungi worked synergistically as a network to degrade cellulose with Streptomyces (0.3045**) as the key community revealed by multi-interaction analysis. Organic matter (-0.415***) and temperature (-0.327***) were key environmental parameters regulating cellulose degradation via influencing ß-glucosidase-producing communities, and ß-glucosidase played a key role in mediating this process. The above results indicated that responses of ß-glucosidase-producing microorganisms to cellulose degradation were reflected at both network and individual levels and multi-interaction analysis could better explain the relationship between variables concerning composting cellulose degradation. The work is of significance for understanding cellulose degradation microbial communities and process during composting of pure corn straw.

Composted biochar affects structural dynamics, function and co-occurrence network patterns of fungi community.

A few researchers have reported enhancing soil physicochemical properties and reducing greenhouse gas emission using biochar-compost mixture as an alternative method to address soil fertility, soil degradation and climate change. However, information about its effects on soil microbiome has rarely been studied. This investigation was on the impact of a combined biochar-compost application on soil physicochemical variables, fungal community composition, function and network patterns in maize at seedling stage (SS), reproductive stage (RS), and maturity stage (MS). The experimental design consists of five treatments: control (CNT), compost (CMP), composted biochar (CMB), compost fortified with biochar (CFWB), biochar (BCH). The results showed that CFWB, CMB, and CMP increased fungal diversity indices (Shannon, Sobs, and Chao) at the RS and MS stages respectively, compared to BCH and CNT. Distance-based redundancy analysis (db-RDA) at genus level indicated that the pH, available nitrogen, and soil organic matter at SS; available phosphorus at RS; Mg, Mn, Fe, and Zn at MS significantly and positively affected the fungi community. Based on the Linear discriminant analysis (LDA) and effect size (LEfSe) analysis, the results revealed that only Cystofilobasidiaceae and Guehomyces were the MS biomarkers; and significantly enriched in CFWB. FUNGuild analysis indicated that organic amendments (CFWB, CMB, CMP, and BCH) suppressed the abundance of plant pathogenic fungi (Edenia and Waitea) compared to CNT. Network analysis showed that CFWB and CMB had a high niche overlap and cross-feeding in their networks compared to other treatments. However, CMP network had more positive links with Saprotroph, Pathotroph-Saprotroph-Symbiotroph, Pathotroph and Pathotroph-Symbiotroph compared with other treatments. This study showed that applying biochar, compost and a mixture of both, positively affected soil fungal communities plus co-occurrence network pattern in a single cropping season. Thus, their application as soil amendments may improve the soil fungi ecosystem, soil health and quality and mitigate climate change.

Microbial community composition, co-occurrence network pattern and nitrogen transformation genera response to biochar addition in cattle manure-maize straw composting.

A better understanding of the microbial group influencing nitrogen (N) dynamics and cycling in composting matrix is critical in achieving good management to alleviate N loss and improve final compost quality. This study investigated the bacterial composition, structure, co-occurrence network patterns and topological roles of N transformation in cattle manure-maize straw composting using high-throughput sequencing. The two treatments used in this experiment were cattle manure and maize straw mixture (CM) and CM with 10% biochar addition (CMB). In both treatments, the bacterial community composition varied during composting and the major phyla included Actinobacteria, Firmicutes, Proteobacteria, Bacteroidetes and Chloroflexi. The phyla Actinobacteria and Proteobacteria were more abundant in CMB treatment while Firmicutes was abundant in CM piles. The metabolic functional profiles of bacteria was predicted using the "phylogenetic investigation of communities by reconstruction of unobserved states" (PICRUSt) which revealed that except for cellular processes pathway, CMB had slight higher abundance in metabolism, genetic information processing and environmental information processing than the CM. Pearson correlation revealed more significant relationship between the important bacteria communities and N transformation in CMB piles compared with CM. Furthermore, network pattern analysis revealed that the bacterial networks in biochar amended piles are more complex and harbored more positive links than that of no biochar piles. Corresponding agreement of multivariate analyses (correlation heatmap, stepwise regression, Path and network analyses) revealed that Psychrobacter, Thermopolyspora and Thermobifida in CM while Corynebacterium_1, Thermomonospora and Streptomyces in CMB were key bacterial genera affecting NH4+-N, NO3--N and total nitrogen (TN) transformation respectively during composting process. These results provide insight into nitrogen transformation and co-occurrence patterns mediating microbes and bacterial metabolism which could be useful in enhancing compost quality and mitigating N loss during composting.

Fungal community succession under influence of biochar in cow manure composting.

This study examined the influence of biochar addition on fungal community during composting of cow manure using high-throughput sequencing. Two treatments were set up, including compost of cow manure plus 10% biochar (BC) and cow manure compost without biochar (CK). Fungal community composition varied obviously during composting in both treatments, and main fungi included Aspergillus, Myriococcum, Thermomyces, Mycothermus, Scedosporium, Cladosporium, and unclassified Microascaceae. Fungal community composition was altered by biochar during composting, especially during the thermophilic and the cooling phase, promoting Aspergillus and Myriococcum while inhibiting unclassified Microascaceae and Thermomyces. Based on linear discriminant analysis effect size analysis, common indicator groups were detected in both composts; however, specific indicator groups were also found in BC treatment, including Clavicipitaceae, Tremellales, Gibberella, and Coprinopsis. Canonical correspondence analysis (CCA) indicated that moisture content, organic matter, C/N, and pH had significant correlation (p < 0.05) with fungal composition in both treatments. However, in compost added with biochar, temperature was not an important factor affecting fungal community (p > 0.05).