The black soldier fly (BSF) is recognised as a valuable insect for mitigating feed and organic waste management challenges. Thus, concerted efforts are being directed toward the promotion of the BSF. Despite the numerous advantages of BSF larvae, there are several critical environmental aspects, particularly its global warming potential, that need to be considered before large-scale adoption due to the complexity of the insect’s value chain. The direct assessment of greenhouse gas (GHG) and ammonia emissions from BSF larvae biotreatment is crucial for conducting a life cycle assessment (LCA) to evaluate the insect products’ environmental performance. This article reviews the emissions of GHG from BSF larvae bioconversion activities based on different gas sensing techniques while highlighting the factors that influence these emissions. Generally, low gas emissions were reported. However, the influence of various factors influencing emissions remains unclear, especially for nitrous oxide. We also analysed LCA studies on BSFL products while emphasising the uncertainties and variabilities among the studies. The wide variation of impact scores reported in the studies suggests that standardised guidelines should be developed to streamline methodical approaches for impact assessments pertaining to system boundaries, functional units, allocation, and system expansion assumptions. We identified several aspects for future improvements to harmonise studies in order to enhance the comparative assessment of the BSFL products.

Phasing out fossil fuels to renewables is currently a global priority due to the climate change threat. Advocacy for biomass use as an energy source requires assessing the quality biomass and ecological impacts of bioenergy supply chains. This study evaluated the quality of biomass residues from orchards and silviculture transported from different Northern and Central Italy locations and the carbon footprint of a biomass power plant. The total greenhouse emissions were calculated based on primary data for 2017 according to the ISO/TS 14067. All the residue samples showed their suitability for biofuel use. Ash content was relatively low on average (3–5% d.m.), except for grapevine residues (18% d.m.). The lower heating value was within the expected range of 15–21 MJ kg⁻¹ for plant species. The average GHG emission from the power plant was 17.4 g CO₂ eq./MJ of electrical energy, with the energy conversion (38%) and transportation of biomass (34%) phases being the main impact contributors. For this study, impacts of residual agricultural residue were about half that of residues from forest management, mainly due to chipping and greater transport distance. Results show that sourcing residual biomass materials for electricity generation close to power plants significantly reduce GHG emissions compared to conventional fossil fuels.

Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

Replacing fossil energy sources with renewable energy sources is a key strategic action to limit environmental issues. To achieve this goal, substitution with biomass is beneficial due to its versatility in various fields. In terms of circular economy and sustainability, the possibility of energy exploitation of residual biomass is particularly desirable in small-medium enterprises. The use of supply chain by-products can improve sustainability and create opportunities for companies. The purpose of this study is to evaluate the suitability of residual biomass of conifers and broad-leaved trees to produce quality pellets using an agri-pellet machine activated by the power take-off of a tractor. This system can be employed at the farm level. Wood biomass of four species was tested; poplar, stone pine, black locust, and oak. Wood chips samples were analyzed to determine their qualitative characteristics following the technical standard ISO 17225-4. Based on the results, different wood blends were created to produce pellets, subsequently characterized according to ISO 17225-2. The analyses carried out on wood chips and pellets were bulk density, moisture, ash content, calorific value, elemental composition, chlorine, sulfur, and heavy metals. In addition, particles size was measured only for wood chips, while the length, diameter, mechanical durability, and ash melting behaviors were determined only for pellets. Some of the analyzed mixtures show acceptable values according to the current ISO technical standards. The values related to the apparent pellet bulk density and the durability test highlight that not all the mixtures are suitable to produce quality pellets. Results also represent a good starting point for future studies.