The implementation of sustainable agro-energy systems that integrate crop, livestock, and bioenergy production is attracting increasing interest from farmers. Livestock produces large amounts of animal manure which can serve as organic fertilizer for crops and pasture growth. However, the nutrients contained in manure can adversely affect air, water, and soil quality and pose a public health risk if not handled properly. Existing manure management practices vary widely on a global scale. Researchers are striving to identify appropriate manure management practices with the aim of environmental protection. Anaerobic digestion of manure and subsequent digestate (DG) processing technologies have been proposed to stabilize manure so that it can be safely used for land applications. DG, which represents digested substrate removed from the anaerobic reactor after recovery of biogas, is a rich source of N, P, K, and S, various micronutrients, and organic matter, the addition of which to the soil can stimulate soil microbial biomass metabolic activities thus improving soil ecosystem function. However, the optimal fertilization properties of DG can be lost if it is neither fully stabilized nor contains biodegradable materials. To overcome these problems, various processing technologies can be used to convert DG into value-added by-products. Composting has been proposed as one such preferred post-treatment that can convert DG into mature, stable, safe, humus- and nutrient-rich compost. Other processing technologies such as thermal drying, gasification, hydrothermal carbonization, pyrolysis, membrane filtration, struvite precipitation, ammonia stripping, and evaporation have also been proposed for DG processing and nutrient recovery from DG. The objective of this review paper was to provide an overview of the current state of the art in DG management regulations and practices and to provide an update on the various processes that have been developed to meet DG stabilization requirements, with a focus on composting as one of the preferred solutions.

The research on lignocellulose pretreatments is generally performed through experiments that require substantial resources, are often time-consuming and are not always environmentally friendly. Therefore, researchers are developing computational methods which can minimize experimental procedures and save money. In this research, three machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM), as well as their ensembles were evaluated to predict acid-insoluble detergent lignin (AIDL) content in lignocellulose biomass. Three different types of harvest residue (maize stover, soybean straw and sunflower stalk) were first pretreated in a laboratory oven with hot air under two different temperatures (121 and 175 °C) at different duration (30 and 90 min) with the aim of disintegration of the lignocellulosic structure, i.e., delignification. Based on the leave-one-out cross-validation, the XGB resulted in the highest accuracy for all individual harvest residues, achieving the coefficient of determination (R²) in the range of 0.756–0.980. The relative variable importances for all individual harvest residues strongly suggested the dominant impact of pretreatment temperature in comparison to its duration. These findings proved the effectiveness of machine learning prediction in the optimization of lignocellulose pretreatment, leading to a more efficient lignin destabilization approach.