In the context of climate change and the circular economy, biochar agricultural and environmental applications have attracted a good deal of attention. Biochar has unique characteristics like surface area, porosity, water-holding capacity, pH, surface charge, and nutrients. This study reviews the biochar production from olive pomace (OP) and olive stone (OS) byproducts, its chemical and physical characterization, and its environmental application. The current review highlights the conditions for biochar production, the effects of pyrolysis temperature, and feedstock type on the physicochemical properties of biochar. High pyrolysis temperature (>500 °C) promotes a high specific surface area, high porosity (especially for OS biochars), and pH as well as the content of ash and fixed carbon, but generates low cation exchange capacity (CEC) and electrical conductivity (EC) and high values of O/C and H/C ratio. OP biochar also presents a high C amount, and ash content, i.e., rich in nutrients and high alkalizing capacity. OP biochar serves as an important source of plant nutrients, especially potassium. After adding both types of biochar, aggregate stability and the amount of water held in soil increase, and bulk density and bioavailability of trace elements decrease. Thus, biochar from olive mill wastes can be a potential plant nutrient reservoir, a good amendment to improve soil properties and long-term carbon sequestration. Results presented in this review can be used to build designer biochars from olive mill wastes to help solve environmental issues (water purification and pollutant remediation) and are suitable for improving soil physical chemistry characteristics and crop growth.

Pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) of humic acids (HAs) from 30 agricultural soils from a volcanic island (Tenerife, Spain) was used to discern the molecular characteristics of soil organic matter (SOM) associated to resilience. For faster perceptual identification of the results, the yields of the pyrolysis products in the form of surface density plots were compared in an update of the Van Krevelen graphical statistical method. This approach, with respect to data reduction and visualization, was also used to collectively represent statistical indices that were obtained after simple and partial least squares (PLS) regression. The resulting plots illustrate different SOM structural domains (for example, carbohydrate- and lignin-derived and condensed lipid). The content of SOM and total mineralization coefficient (TMC) values can be well estimated from the relative abundance of 57 major pyrolysis compounds: SOM content and composition parallels the accumulation of lignin- and carbohydrate-derived structures (lignocellulosic material) and the depletion of condensed polyalkyl structures. In other words, in the volcanic ash soils that were studied, we found that the higher the amount of SOM, the lower its quality in terms of resilience. Although no cause-and-effect is inferred from this fact, it is evident that the resistance to biodegradation of the SOM is related to its molecular composition.

In the current global change scenario, valuable tools for improving soils and increasing both agricultural productivity and food security, together with effective actions to mitigate the impacts of ongoing climate change trends, are priority issues. Soil Organic Carbon (SOC) acts on these two topics, as C is a core element of soil organic matter, an essential driver of soil fertility, and becomes problematic when disposed of in the atmosphere in its gaseous form. Laboratory methods to measure SOC are expensive and time-consuming. This Systematic Literature Review (SLR) aims to identify techniques and alternative ways to estimate SOC using Remote-Sensing (RS) spectral data and computer tools to process this database. This SLR was conducted using Systematic Review and Meta-Analysis (PRISMA) methodology, highlighting the use of Deep Learning (DL), traditional neural networks, and other machine-learning models, and the input data were used to estimate SOC. The SLR concludes that Sentinel satellites, particularly Sentinel-2, were frequently used. Despite limited datasets, DL models demonstrated robust performance as assessed by R2 and RMSE. Key input data, such as vegetation indices (e.g., NDVI, SAVI, EVI) and digital elevation models, were consistently correlated with SOC predictions. These findings underscore the potential of combining RS and advanced artificial-intelligence techniques for efficient and scalable SOC monitoring.