Multivariate Analysis of the Determinants of the End-Product Quality of Manure-Based Composts and Vermicomposts Using Bayesian Network Modelling

Abstract

Previous studies indicated that the quality of tropical composts is poorer than that of composts produced in temperate regions. The aim of this study was to test the type of manure, the use of co-composting with green waste, and the stabilization method for their ability to improve compost quality in the tropics. We produced 68 composts and vermicomposts that were analysed for their C, lignin and NPK contents throughout the composting process. Bayesian networks were used to assess the mechanisms controlling compost quality. The concentration effect, for C and lignin, and the initial blend quality, for NPK content, were the main factors affecting compost quality. Cattle manure composts presented the highest C and lignin contents, and poultry litter composts exhibited the highest NPK content. Co-composting improved quality by enhancing the concentration effect, which reduced the impact of C and nutrient losses. Vermicomposting did not improve compost quality; co-composting without earthworms thus appears to be a suitable stabilization method under the conditions of this study because it produced high quality composts and is easier to implement.

A Review of Composting Process Models of Organic Solid Waste with a Focus on the Fates of C, N, P, and K

“…In addition, it can be found that the researchers working on these models gradually began to pay attention to the significance of open science for scientific progress. For instance, Faverial et al obtained the highest score in the checklist and their paper can be openly accessed [15]. Another treatment technology, anaerobic digestion (AD), a unified and open model of Anaerobic Digestion Model No.…”

“…However, in terms of accuracy, the ANN model performed better in all three articles. Other models, such as Bayesian network models [15] and Genetic algorithms [65], are all used in data-driven models.…”

“…The selection of input is an important step in developing the data-driven model. As can be seen in Table 3, pH is the most commonly used input variable (n = 7), which has a great influence on the decay, odor emission, nutrient conversion, and loss rate in the composting process [15,59]. Others, such as moisture content (n = 6), EC (n = 5), C/N (n = 4), and temperature (n = 3), are also commonly used as input variables.…”

“…The checklist could efficiently describe the fates N, P, and K during composting. It was not aimed to completely distinguish the advan and disadvantages of models, but largely focused on checking whether these mod the scope and subject of the review, and how well they fitted the procedures model can be seen from Figure 4 that the research of Faverial et al was more in line wit scope of the review, while the overall modeling was also in line with the specifica having an excellent performance in accuracy [15]. The paper of Chen et al, a confe paper with limited space, also attracted our attention, in which their scores were aff as some modeling procedures may not be described in details [71].…”

“…In China, about 76% of the poultry and livestock manure collected by intensive farming was processed through composting in 2015 [12], which can promote the organic fertilizer production industry and increase the circulation of regional nutrients [13]. Even though fruitful results have been achieved in the research on composing, there remains a large challenge when microorganisms convert complex substrates into ultimately useful products in the composting process, in which some by-products, such as Ammonia (NH 3 ), Carbon dioxide (CO 2 ), Methane (CH 4 ), Nitrous oxide (N 2 O), etc., are produced to burden the atmosphere [14,15]. The accumulation of P in surface soil can lead to the transfer of Phosphorus (P) to groundwater, which becomes an environmental concern during the compost application [16].…”