Nelle moderne pratiche di uso del suolo le attività agricole e selvicolturali vengono prevalentemente gestite in modo mutualmente esclusivo. Non era così fino a non molti decenni fa: ad esempio, in tutto il bacino del Mediterraneo per millenni hanno avuto estensione molto ampia i sistemi agrosilvopastorali, principalmente con alberi di querce. In Italia, la consociazione tra alberi forestali, seminativi, colture orticole, prati, pascoli, alberi da frutto, olivi e vite hanno dato luogo a veri e propri paesaggi agricoli arborati, di particolare qualità anche estetica (si pensi ai dipinti dei Macchiaioli toscani del XIX secolo dove alberi, boschetti, campi e prati appaiono sempre strettamente interconnessi). Soprattutto a partire dal secondo dopoguerra del secolo scorso, la rapida e capillare diffusione dell'agricoltura intensiva, basata sulla meccanizzazione e sulla chimica di sintesi, ha cambiato i paradigmi delle attività agricole, con una significativa semplificazione degli agroecosistemi verso forme monocolturali. Peraltro, alcune aree geografiche ancora conservano importanti sistemi agroforestali: ad esempio, il Piemonte con intercolture di mais, soia o grano tra i pioppi, l'Alto Adige con il pascolo tra i larici e la Sardegna con quello nelle sugherete, l'Umbria con i seminativi tra gli olivi e la Campania con le associazioni di colture orticole e noceti da frutto e da legno. Questi esempi evidenziano come la coltivazione di alberi forestali e da frutto, seminativi e animali domestici sulla stessa superficie sia una opzione di uso del suolo concretamente percorribile anche in un moderno contesto di gestione: a essi si ispira la moderna agroforestazione, basata sull'inserimento/mantenimento di basse densità di alberi forestali nonché di filari arborei e arbustivi nell'ambito dei sistemi agricoli. Molti imprenditori sono interessati a una simile modalità di uso del territorio: le principali motivazioni sono il mantenimento della fertilità dei suoli, la riduzione degli apporti energetici, il rinforzo della lotta biologica, la diversificazione dei prodotti. In linea di principio, i sistemi agroforestali determinano un più efficiente uso delle risorse naturali rispetto a quelli monocolturali: nonostante ciò, le prassi della moderna agroforestazione sono ancora relativamente poco valorizzate su larga scala. Alla luce di ciò, questa monografia vuole offrire un aggiornato riferimento conoscitivo e di analisi sul tema, in forma di linee guida, a supporto degli imprenditori agricoli, dei tecnici professionisti e dei soggetti tecnico-istituzionali competenti per il settore.

Il ruolo dell'agroforestazione nel paesaggio agricolo e della biodiversità, sia nell'agricoltura passata sia in quella attuale. E' stato esposto Il ruolo dell'AIAF, ass. ita. agroforestazione, e della Sisef, soc.sci. ita. di ecologia forestale selvicoltura-Gruppo di lavoro- Arboricoltura da legno ed agroselvicoltura, dell'ambito della tematiche suddette. La presentazione è avvenuta durante la tavola rotonda "Rapporto tra agroecologia, biodiversità e paesaggio" coordinata da Federica Luoni (Lipu), con Simona Bonelli (Università di Torino), Costanza Pratesi (FAI ), Antonio Longo (Politecnico Milano), Chiara Bassignana (Università di Scienze Gastronomiche di Pollenzo), Francesco Sottile (Università di Palermo - Slowfood), Marilda Dhaskali (Agricolture Policy Offer BirdLife Europe) Marino Quaranta (CREA Bologna) e Pierluigi Paris (CNR IRET-AIAF-Sisef)

La presentazione riporta lo stato dell'arte sulla coltivazione del noce (Juglans spp) in Italia ed Europa, per la produzione di legname di qualità per l'industria del mobile arredo, in base alle esperienze di ricerca dell'autore e della principale bibliografia nazionale ed internazionale. Sono stati esposte 5 principali tematiche. 1. La storia plurisecolare della coltivazione del noce comune (J. regia) e le altre specie del genere Juglans nei continenti del mondo, indicando gli ibridi da incroci. 2. Gli attuali finanziamenti della Politica Comunitaria Agricola (PAC 2023-27) per sostenere gli agricoltori nella realizzazione di piantagioni da legno e per la sostenibilità ambientale. 3. Le principali fonti genetiche del materiale di base per i semenzali da mettere a dimora. 4. Le condizioni ottimali del sito d'impianto per qualità del suolo e clima più adatti per il successo tecnico della piantagione legnosa. 5. I modelli colturali che possono essere scelti dall'agricoltore tra: piantagioni mono-specifiche/monoclonali (con i recenti cloni di noci ibridi disponibili in commercio); impianti misti di consociazione tra più specie legnose; impianti agroforestali, di consociazione tra alberi da legno e colture erbacee. Quest'ultima opzione sembra essere quella più adatta al contesto pedoclimatico e socio-economico italiano, per ridurre l'import di legno di qualità da altri continenti, e per mitigare la Crisi Climatica ed ambientale

Italy is the second largest extra-virgin olive oil (EVOO) producer within the European Union. De-spite its importance in preserving rural landscape and in supporting household economy, the EVOO sector faces several constraints due to high management costs, small farm size, lack of co-operation and investment, production vulnerability, and farmers' ageing. Such a number of weak points suggests the need to identify and adopt innovative approaches, at both the farm and oil mill levels. In order to address these priorities, a fuzzy cognitive mapping (FCM) survey was carried out in Umbria region, central Italy, involving key local stakeholders of the EVOO value chain in the Orvieto district. Based on stakeholders' perception and knowledge, this paper aims to identify and evaluate the most relevant components of the local olive oil value chain, and predict scenarios responding to hypothetical changes of the same components. These stakeholders were firstly in-vited to each build an individual fuzzy cognitive map and then, grouped all together, build a joint fuzzy cognitive map. Finally, the maps represented both the individual and the grouped stake-holders' perceptions. The maps were translated into adjacency matrices in order to create an FCM model by applying the software "Mental Modeler". In total, 24 participants, including practition-ers, multipliers, researchers, suppliers and members of local administration, participated in the survey. The component analysis and the scenario analysis highlighted several priority issues: to preserve the ecosystem functioning, to implement cooperation, innovation and education, to adapt and mitigate climate change. The main novelty of this study is that all stakeholders' catego-ries in the EVOO sector recognize several challenges to sustain the EVOO value chain, in particular, climate change adaptation and mitigation.

Il contrasto alla Crisi Climatica richiede forti cambiamenti socio-economici con l'ampio uso di materie prime ed energie rinnovabili, come il legno, e nonché vasti programmi di forestazione minimizzando il consumo di suolo agricolo, nonché modelli colturali con input chimici contenuti. L'agroforestazione con i nuovi cloni di pioppo a Maggiore Sostenibilità Ambientale (MSA) risponde alle suddette esigenze con interessanti margini di guadagno per gli agricoltori

The purpose of this research was to fill the identified gap on financial data of Sida hermaphrodita (L.) Rusby (Sida) and Silphium perfoliatum L. (Silphium), two perennial bioenergy crops that potentially provide a more sustainable alternative/complement to other bioenergy crops. Using discounted cash flow analysis, the Net Present Values of Sida and Silphium were compared to a rotation of other arable crops including maize, and the two energy crops of short rotation coppice and Miscanthus. The analysis was completed using the SidaTim analysis tool for the UK, Italy, Germany and Poland, producing a total of four independent models. The results showed that with no subsidies, cultivating Sida was unattractive in all four countries relative to other crop options. However, Silphium, was an economically viable option in each country. Both Sida and Silphium can offer greater environmental benefits than other arable crops, and the profitability of each crop would be further enhanced if additional payments for such public services were made to farmers, and if there were secure markets for the sale of the biomass. This study is the first comparative economic analysis in West and Central Europe of the two novel energy crops in comparison to more common energy crops and an arable rotation

As agroforestry systems have a high potential for food security, biodiversity and socio-economic sustainability and climate change adaptation and mitigation, they are currently being promoted or traditionally maintained in many regions of the world. In fact, agroforestry promotes multifunctional and resilient agriculture, with positive results in terms of ecosystem services. Due to the complexity of the tree-based agricultural systems an appropriate knowledge of its components, both in terms of spatial extent and functional relationships is fundamental. The mapping and monitoring of such complex systems are essential for a proper and sustainable management of resources as well as for planning of mitigation and adaptation measures against the rising environmental risks. The current estimation of agroforestry in European Union, according to LUCAS database (Eurostat 2015) is about 15.4 million ha, corresponding to about 3.6% of the territorial area and 8.8% of the utilised agricultural area (den Herder et al., 2017). Going down to the national level, 4,7% of total Italian area has been estimated as agroforestry, with main distribution in Central and Southern Italy. In this study, we aim to estimate agroforestry with more accuracy by exploiting the Google Earth Engine (GEE) platform. In order to achieve this target, we took a case study within a rural area in Central Italy, belonging to the "Bolsena Lake Bio-District". A bio-district is a civil local agreement, recognised by national and regional regulations, targeted to foster sustainability in rural areas by means of organic and high natural value farming systems. The Google Earth Engine cloud-computing platform (GEE) allows users a quick and seamless access to the standard satellite imagery without downloading the actual scenes, thus providing the means to build time series of indices counting hundreds of records in almost no time. GEE allows users to perform geospatial analysis from local to planetary scale based on Google's cloud infrastructure in a very short time, by accessing data from a large repository of publicly available geospatial dataset, including more than forty years of historical imagery, such as the entire Landsat archive as well as the complete Copernicus Sentinel archive and a variety of earth science-related datasets. In order to estimate and map agroforestry in the study area, we developed and tested an openly available GEE script, based on our previously studies for mapping Trees Outside Forest (TOF) in agroforestry landscapes (Chiocchini et al. 2019; Sarti et al. 2021). The workflow, in a nutshell, consists in singling out trees from a temporal series of images via optical indices thresholding, then extracting trees out of forest polygons (TOF) and classifying them according to their size and shape.

The Google Earth Engine cloud-computing platform (GEE) allows users to perform geospatial analysis from local to planetary scale based on Google's cloud infrastructure in a short time. Users can access and analyse data from a large repository of publicly available geospatial dataset, including more than forty years of historical imagery, such as the entire Landsat archive as well as the complete Copernicus Sentinel archive, and a variety of earth science-related datasets. Applications of GEE are growing in many fields of earth science, such as global forest changes, effects on the global water cycle, land cover/land use changes, flood mapping, urban mapping and so on. In this study we used GEE for a just apparently trivial problem: mapping Tree Outside Forest (TOF). Since small woods, tree hedgerows, scattered and isolated trees are key TOF features of rural and urban landscapes all around the world and provide a variety of products and ecosystem services essential for human well-being, the interest in estimating and mapping TOF coverage is increasing. For these reasons, filling the lack of information on TOF extension is crucial to develop effective agro-environmental measures and rural development policies. We followed the approach proposed in our recent studies, based on Sentinel-2 imagery, consisting in: 1) an automatic identification of tree covered surface, by applying a statistical threshold on several vegetation related optical indices (NDVI, EVI, Negative Luminance etc.), 2) an object-based image analysis to classify TOF elements, 3) a ground truth validation process. We developed an openly available GEE script performing the following steps: 1) retrieval of a collection of Sentinel-2 images between two dates in a dynamically chosen Area of Interest (AoI), with a user-defined maximum cloud coverage. 2) Extraction of the minimum for each band, obtaining a single image, from which green, blue, red and near-infrared (both 8 and 8A bands) are extracted and clipped to the AoI. 3) Evaluation of an optical index and relative histogram for manually choosing the threshold value for tree cover identification. 4) Creation of a polygon coverage for trees, taking the optical index values that exceed the given threshold. The polygons are then equipped with a shape factor and a pixel count field. 5) Extraction of TOF polygons from trees, based on shape and size. 6) Graphical GIS-like presentation of trees and TOF polygons over the optical indices and Sentinel-2 layers. All the layers can be exported as georeferenced shapefiles or geoTiffs, for ground truth validation and further off-cloud spatial analysis. In this study we exploited many capabilities of GEE. Cloud-computing speeds-up the conventional pre-processing phase to an unprecedented level (images retrieval, fusion and index computation is almost instantaneous and visually driven) as well as the processing phase (vegetation index extraction, histogram evaluation, thresholding and coverage vectorisation), including some geospatial raster (filtering, thresholding, comparison) as well as vector analysis. The only operator-based step consists in the vegetation index choice and threshold selection for the trees identification. The identification rationale consists in the presence of a distinguished peak in the vegetation index of the AoI, which is to be selected by the operator within the GEE script execution, mouse-clicking on the histogram graph. Our script has been developed and tested on an agroforestry landscape in Umbria, central Italy, but it could be run potentially all over the world, choosing the best-performing optical index or linear bands combination. Further improvements could include a fully automated vegetation index choice and threshold selection, limiting the operator's role to the choice of the AoI. The ground truth validation is meant to be performed manually, but it could be easily implemented within the GEE platform itself, uploading ones' vector validation layers as so-called GEE assets, from ordinary classified shapefiles.

I residui della pioppicoltura (ramaglie, cimali ed apparati radicali) attualmente vengono principalmente recuperati a fini energetici sotto forma di sminuzzato. per un'ulteriore valorizzazione economica e possibile riimpiego in azienda, la trasformazione in biochar rappresenta una possibile prospettiva che merita valutazioni agronomiche e biochimiche.

La pioppicoltura in Italia è un comparto di eccellenza per la produzione di legno ad uso industriale ed energetico. Questo contributo intende analizzare lo stato della filiera produttiva del pioppo in Italia, illustrando le principali prospettive per la crescita della produttività e della sostenibilità del settore, in coerenza con una prospettiva di intensificazione sostenibile. Oltre all'aspetto produttivo, la pioppicoltura ha anche una elevata valenza ambientale. Lo sviluppo e la diffusione di cloni a maggiore sostenibilità ambientale, i nuovi moduli colturali, le prospettive offerte dalla meccanizzazione e dalla selvicoltura di precisione, i prodotti della bioeconomia e della bioraffineria, e i benefici ambientali in termini di sequestro della CO2 e contrasto all'inquinamento dell'aria e dei suoli rendono la pioppicoltura una tessera importante della filiera forestale italiana.

This study proposes an automated method for distinguishing trees (T) from no-trees (NT) by means of optical data. We make use of an optical approach based on a statistical threshold to detect T areas on visible and near infrared bands. An object-based image classification allows to detect three kinds of tree out of forest (TOF) structures: forest patches (FP), isolated trees (IT), tree hedgerows (THR), distinguished from forest (F). Ground truth validation allows estimating the accuracy of classification. Four optical bands and six spectral indices are compared detecting images' T areas: B2, B3, B4 and B8 bands, Negative Luminance (NL), Normalized Difference Vegetation index (NDVI), Green NDVI (GNDVI), Blue NDVI (BNDVI), Panchromatic NDVI (PNDVI) and Enhanced Vegetation Index (EVI). NL shows a relatively better capability for TOF detection and classification, with overall accuracy (OA) exceeding 92% and p-value = 10^-5. Experiments were conducted on optical data acquired by Sentinel-2 in 2016 over the Alfina highland, central Italy. The tree characteristics were extracted exploiting GNU Octave Image Package. Our results show that this new approach could be extended to the detection and mapping of TOF within large areas of agroforestry landscape.

The Italian poplar cultivation has recently turned its attention to 'MSA' clones with 'Greater Environmental Sustainability', characterized by resistance to the principal biotic stresses, requiring less pesticides. To date, there is limited information on the drought tolerance of these clones. This work aimed to determine the degree of tolerance/susceptibility to drought in poplar clones with 'Greater Environmental Sustainability' than the reference clone 'I-214'. Firstly, a preliminary experiment with three different clones under drought stress showed a statistically significant alteration on morphometric and physiological parameters than under normal conditions. The physiological and biochemical indices related to drought tolerance of 'Neva' clone were improved, resulting in greater tolerance than 'I-214' and 'San Martino' clones. A second experiment was conducted by expanding the population of clones considered. The results showed that the relative water content was not significantly different among the control treatments and the drought-stressed ones. In particular, the analysis showed a greater degree of tolerance to the water stress of the 'Neva' clone and the 'Tucano' clone. On the contrary, the 'Lena' and 'San Martino' clones were more susceptible to water deficiency stress. The water stress significantly decreased photosystem II performance, the stomatal conductance, and transpiration compared to the control one. Future analyses will be focused on the characterization of clones and genetic improvement for drought tolerance.

Il contributo al convegno-incontro tecnico, riportata le più recenti esperienze di ricerca sui modelli colturali dell'arboricoltura da legno del pioppo, in alternativa alla monocoltura clonale che attualmente domina l'orientamento dei pioppicoltori, soprattutto nella Pianura Padana, con negative conseguenze ambientali e di sostenibilità produttiva

Non disponibile

Vegetation indices, water indices, brightness and form indices, to name only a few, are long time classics for land cover use and land cover change detection. Prior to cloud computing, the standard workflow started from scenes selection by time, cloud filtering, image registration and, finally, indices evaluation. Working on local workstations, no matter how performant they are, other than being time-consuming, is often critical in terms of both computational load and mass storage requirements. Imagery fine-tuning still requires the possession of the physical files but cloud services can speed up to unprecedented levels most of the standard machinery of indices assessment. The Google Earth Engine platform allows quick and seamless access to the standard satellite imagery without downloading the actual scenes, thus providing the means to build time series of indices counting hundreds of records in almost no time. Furthermore, the Earth Engine platform supplies on-board raster algebra, so that it is not even necessary to download the indices for further calculations. The peri-urban landscape is characterised by land cover changes, often detectable through indices differences. The spatial scale needed by this kind of environment could benefit from the resolution of the current state of the art publicly available satellites, mainly the Sentinel-2 MSI and the Landsat-8 OLI sensors. At the price of some coarse-graining, older Landsat imagery is also available. We show that with a few lines of code users can highlight the putative land changes, creating a sketch land cover differential map.

Cities host more than half of the world's population and this trend is expected to increase in the coming years. Due to global warming, events such as heat waves are increasingly frequent and the effects on human health are very worrying. High temperatures are more prevalent in urban areas where the large percentage of impermeable surfaces contribute to the so-called 'heat island' effect. In this context, urban trees and forests play an important role in air temperature mitigation through evapotranspiration and shading. However, the assessment of their contribution to urban microclimate regulation is often difficult to quantify through direct observations. The use of models allows to evaluate the spatial distribution of air temperature and humidity, based on the presence of tree cover, impervious surface, topography, land use, and weather data. In this study, we applied for the first time on Naples the i-Tree Cool Air model, a spatially explicit air temperature model simulating the effects of land cover changes using the water and energy budget that explicitly accounts for vegetation processes. We modeled air temperature at different times, during summer heat waves, of areas with an increasing tree cover and compared results with direct measurements. The prolonged summer drought in the Mediterranean area significantly reduces the evapotranspiration of trees and thus their contribution to air cooling. A proper planning of green areas in the city, using decision-support tools as the presented model, could increase the human thermal comfort.

On behalf of the EURAF2020 Scientific and Organizing Committees, we are very pleased to introduce the rich collection of research on agroforestry illustrated in this book of abstracts and presented within the 5° European Agroforestry Conference. Unfortunately, as we all know, the COVID-19 pandemic has forced us to meet only remotely, despite all the efforts of our local and national organizers to hold the conference in presence. We are conscious about the completely different dimension, which does not allow participants to meet, discuss and live the conference supported by an environment socially vibrant and rich of cross-cultural stimuli as the real Sardinia can offer. Nevertheless, in accordance with the mission of the European Agroforestry Federation, EURAF, to promote agroforestry knowledge, we wish to support the sharing of data presented and solicit a fruitful scientific confrontation on agroforestry issues. This book is the result of a long and rigorous work performed by the authors (about 230 abstracts sent from 5 continents and 37 countries) and members of the Scientific Committee. The book will be one of the tools supporting such confrontation we are glad to foster from the heart of the Mediterranean.

Extended abstract, Book of Abstracts, EURAF2020, Nuoro, Italy, May 17-17, 2021. Online confenrece

Small woods, tree hedgerows, scattered and isolated trees, are also known as Trees Outside Forest (TOF). TOF have an important role in agroforestry landscapes, enhancing their ecological connectivity, hosting biodiversity and having significant impact on biomass and carbon stocks. The identification and classification of TOF on a small area are easy to be accomplished. However, identifying, classifying and mapping TOF at regional or national level are complex, expensive and time-consuming tasks. Precise and fast techniques to estimate the agroforestry surfaces at both regional and national levels are needed. Despite the increasing number of studies combining remote sensing and field surveys for the identification and classification of TOF, guidelines for TOF inventory and mapping in agroforestry systems are still lacking. Furthermore an accurate and objective estimate of the extent and geographical distribution of agroforestry systems in Europe is crucial for the development of supporting policies. In this study we compare the use of Synthetic Aperture Radar (SAR) and optical data, derived from the Sentinel mission dataset, for detecting, classifying and mapping TOF in Italian traditional agroforestry landscapes. We tested the methodology in two areas of interest located in Umbria region (central Italy) where oak trees and hedgerows coexist with crops.