Roberto Calisti

The accurate estimation of tree canopy volume is fundamental in precision agriculture for quantifying vegetation structure, biomass, and productivity in perennial cropping systems. This study investigates a shadow geometry approach for estimating olive tree canopy volumes from a single, very high-resolution WorldView-3 multispectral image. The method integrates multispectral classification for canopy and shadow delineation with a geometric model that infers canopy height from shadow measurements, accounting for solar position and terrain morphology. Two classification strategies were evaluated: object-based image analysis (OBIA) and pixel-based (PB) classification, each applied to the original eight-band multispectral image and to a derived dataset enriched with vegetation indices (NDVI—Normalized Difference Vegetation Index; NDRE—Normalized Difference Red Edge Index) and principal component analysis (PCA) components. The canopy volume was estimated by integrating classified canopy and shadow areas with shadow-derived canopy height. The methodology was tested in a Mediterranean olive orchard and validated against UAV-derived point clouds for approximately 700 trees. The results indicate that the approach captures spatial variability in canopy structure. The Object-Based Image Analysis (OBIA) applied to filtered PCA-enhanced imagery achieved the highest accuracy in canopy volume estimation (RMSE = 2.04 m³; R² = 0.56), outperforming the alternative pixel-based (PB) classification applied to the original multispectral data. Overall, the study demonstrates the potential of single-image WorldView-3 data for rapid and scalable three-dimensional canopy characterization in precision agriculture.

Measuring tree heights is a critical step for assessing ecological and agricultural parameters, including biomass, carbon stock, and canopy volume. In extensive areas exceeding a few hectares, traditional terrestrial measurement methods are often prohibitively expensive in terms of time and cost. This study introduces a GIS-based methodology for estimating olive tree (Olea europaea L.) heights using very-high-resolution (VHR) satellite imagery. The approach integrates a mathematical model that incorporates slope and aspect information derived in a GIS environment from a large-scale Digital Elevation Model. By leveraging sun position data embedded in satellite image metadata, a dedicated geometric model was developed to calculate tree heights. Comparative analyses with a drone-based 3D model demonstrated the statistical reliability of the proposed methodology. While this study focuses on olive trees due to their unique canopy structure, the method could also be applied to other tree species or even to buildings and other vertically developed structures on the ground. Future developments aim to enhance efficiency and usability through the creation of a specialized GIS tool, making it a valuable resource for environmental monitoring, sustainable agricultural management, and broader spatial analysis applications.

This study deals jointly with three aspects of environmental, agricultural and energy sustainability: (a) Biogas is a booming energy source worldwide, resulting in an increasing production of digestate, its main by-product; (b) The extraction of peat, mainly used for nursery substrates, is being banned due to the destruction of natural habitats and release of GHGs; (c) Compost can represent a replacement of peat and contributes to the containment of GHGs. This study has verified how a compost obtained from digestate can be used as a substitute for peat in the nursery sector. While previous studies have evaluated compost use on just one species at a time, this study compared the same compost on two very different species: olive tree and hazelnut tree, both with growing interest for new tree plantings. Two concentrations of compost in the potting substrate of nursery seedlings were evaluated: 30% and 45% by weight, measuring the effect on some growth parameters during the growing season. The trials showed responses positive for olive and substantially negative for hazelnut: olive trees manifested better growth parameters with 45% compost, as opposed to hazelnut, where the addition of 45% compost worsened all growth parameters. A general conclusion can be drawn: in the nursery sector, compost can be used to replace peat, but this replacement can almost never be 100 percent, having instead to calibrate the percentage of replacement according to the characteristics of the compost and the individual edaphic needs of the plant species.

This study simultaneously addresses two issues: (a) defining what counts as ‘small farms’ in the rose sector, taking the geographical and socioeconomic context into account and (b) whether the requests for certification form barriers for small farms. We focus on small farms, as they are of fundamental importance for social and economic development and significantly contribute to the environmental sustainability of agriculture and land use. An agent-based model is used for analyzing an agricultural production and supply chain. The model identifies the minimum farm size needed to cover increased costs due to sustainability certifications. The model is applied to the case study of rose production in Kenya. Kenya is one of the world’s leading flower producers. Almost all Kenya’s floricultural production is exported, and the export of stem roses accounts for about 80% by weight of Kenya’s floricultural exports. Environmental and social sustainability certification is increasingly required for farms, especially those in developing countries that want to export their products. Our findings suggest that sustainability standards disadvantage small Kenyan rose farms and constitute a further obstacle to their entry into the international rose market. In this specific context, standards limit market access for farms smaller than 4 hectares. The agent-based model proposed in this study can be adjusted to help determine the definition of ‘small farms’ in need of extra support in other sectors.

The correct development of the composting process is essential to obtain a product of high value from organic wastes. Nowadays, some composting mixture parameters (i.e., air-filled porosity, moisture and the C/N ratio) are used to optimize the composting process, but their suitability is still debated because the literature reports contrasting results. This paper aimed to find other parameters that control the correct development of composting. The relationship between these and the compost quality was then verified. Twelve different composting mixtures were prepared using different organic wastes and bulking agents and were aerobically treated in a 300 L composter. The physico-chemical and chemical parameters of initial mixtures were analyzed, with particular regard to the total and water-extractable forms of organic C and N and their ratios and correlated with the temperature measured during composting. A positive correlation between temperature parameters during the active phase and soluble forms of N in the initial mixtures was found. A high total organic C to soluble N ratio in the composting mixtures was correlated with the low quality of the compost produced. Based on the results, a minimum content of WEN (water-extractable N) (0.4% w/w) or a TOC/WEN (total organic C/WEN) ratio in the range of 40–80 was recommended to ensure the correct development of the process and to produce compost of high quality.

This study aimed to investigate the relationship among CO₂ emissions, exports of goods and services, and foreign direct investment (FDI) inflows in countries in the MENA region over the period 2002–2014. To that end, spatial panel simultaneous equations model based on the adjacency-based and distance-based weight matrices was used. The findings indicated that the results of spatial panel simultaneous equations with distance-based weight matrix were more fitting than those with adjacency-based weight matrix. The empirical findings approved a two-way linkage between CO₂ emissions and exports and a one-way linkage between CO₂ emissions and FDI inflows and also, between FDI inflows and exports. Furthermore, the existence of the spatial correlations among the CO₂ emissions, exports and FDI inflows across countries was confirmed. It was further indicated that fossil fuel energy consumption was the main determinant of CO₂ emissions, and accession to the WTO played a major role in enhancing exports and FDI inflows. The present study can provide new insights for policymakers and planners to not only consider the economic benefits of exporting goods and services and FDI inflows, but also attend their environmental impacts on local and neighboring countries.

Life Cycle Assessment (the systematic analysis of the environmental impact of products during their entire life cycle), Carbon Footprint and Water Footprint assessments play an important role in decision-making processes. These assessments can help guide land management decisions and will likely play a larger role in the future, especially in natural areas with high biodiversity. Agriculture is a substantial consumer of fresh water, so it is important to identify causes and possible solutions to optimize agricultural water use. Water footprint assessments consider water consumption from several points of view and aid in reaching Sustainable Development Goals. Olive trees are a widespread agricultural crop growing in the Mediterranean Basin and are particularly important in the Umbria region in Italy. This paper estimates the water footprint impact related to the production of 1 kg of olives in a rainfed olive orchard managed using low environmental impact techniques. Eleven years of data collection (meteorological data, olives yield data, processes data) are analyzed for typical rural conditions. The results show that local management techniques have lower water requirements than standard international usages. These results can be used to improve and to further explore agricultural water use.

A useful way of promoting sustainable food consumption is to consider the spread of food retail operations focused on food diversification, food specialization, and fresh and local products. These food shops are generally small, which is a great problem for survival against ruthless competition from supermarkets. Our research objective was to construct a simulation with an agent-based model, reproducing the local food consumption market and to investigate how a new, small food retailing shop interacts with this market. As a case study, the model simulates the opening of a small farmers' market. The intent of the model is to reproduce the current status of consumption for food products within a certain territorial context and given time period, and to investigate how consumers' behaviour changes with the opening of the new shop. As a result, we could predict changes in consumers' habits, the economic positioning of new, small shops and their best location. This information is of considerable interest for farmers' markets and also for policymakers.