Exploring agricultural waste biomass for energy, food and feed production and pollution mitigation: A review

Globally agricultural production system generates a huge amount of solid waste. Improper agri-waste management causes environmental pollution which resulted in economic losses and human health-related problems. Hence, there is an urgent need to design and develop eco-friendly, cost-effective, and socially acceptable agri-waste management technologies. Agri-waste has high energy conversion efficiency as compared to fossil fuel-based energy generation materials. Agri-waste can potentially be exploited for the production of second-generation biofuels. However, composted agri-waste can be an alternative to energy-intensive chemical fertilizers in organic production systems. Furthermore, value-added agri-waste can be a potential feedstock for livestock and industrial products. But comprehensive information concerning agri-waste management is lacking in the literature. Therefore, the present study reviewed the latest advancements in efficient agri-waste management technologies. This latest review will help the researchers and policy planners to formulate environmentally robust residue management practices for achieving a green economy in the agricultural production sector.

Urban soils as a spatial indicator of quality for urban socio-ecological systems

The development of criteria and indicators to quantify the transition to sustainability of the urban socio-ecological systems quality is determinant for planning policies and the 21st century urban agenda. This study models the spatial variation in the concentration and distribution of some macronutrients, micronutrients, and trace nutrients in the soil of a high-altitude city in the Andes. Meanwhile, machine learning methods were employed to study some interactions between the different dimensions that constitute an urban socio-ecosystem that caused these variations. We proposed a methodology that considered two phases: a) field work to collect data on 300 soil samples; laboratory analysis to measure the concentrations of 24 macronutrients, micronutrients, and trace nutrients; and the design of geophysical, spectral, and urban co-variables; b) statistical and geo-informatics analysis, where multivariate analysis grouped the elements into factors; and, machine learning integrated with co-variables was applied to derive the intensity of each factor across the city. Multivariate statistics described the variation in soil co-concentrations with a moderate percentage (42%). Four factors were determined that grouped some of the analyzed elements, as follows: F1 (Zn, S, Cu, Pb, Ni, and Cr), F2 (Ba, Ag, K, In, and Mg), F3 (B, V, Li, and Sr), and F4 (Si and Mn). The percentage R² out-of-bag of the spatial model were: F1 = 20%, F2 = 8%, F3 = 14%, and F4 = 10%. Our outputs show that the enrichment and contamination by anthropogenic factors, such as the increase in population density, land use, road network, and traffic generated by fossil fuel vehicles, should be prioritized in urban planning decisions.