A public decision support system for the assessment of plant disease infection risk shared by Italian regions.

Integrated pest management (IPM) practices proved to be efficient in reducing pesticide use and ensuring economic farming sustainability. Digital decision support systems (DSS) to support the adoption of IPM practices from plant protection services are required by European legislation. Available DSSs used by Italian plant protection services are heterogeneous with regards to disease forecasting models, datasets for their calibration, and level of integration in operational decision-making. This study presents the MISFITS-DSS, which has been jointly developed by a public research institution and nine regional plant protection services with the objective of harmonizing data collection and decision support for Italian farmers. Participatory approach allowed designing a predictive workflow relying on specific domain expertise, in order to explicitly match actual user needs. The DSS calibration entailed the risk of grapevine downy mildew infection (5-point scale from very low to very high), and phenological observations in 2012-2017 as reference data. Process-based models of primary and secondary infections have been implemented and tested via sensitivity analysis (Morris method) under contrasting weather conditions. Hindcast simulations of grapevine phenology, host susceptibility and disease pressure were post-processed by machine-learning classifiers to predict the reference infection risk. Results indicate that IPM principles are implemented by plant protection services since years. The accurate reproduction of grapevine phenology (RMSE = 4-14 days), which drove the dynamic of host susceptibility, and the use of weather forecasts as model inputs contributed to reliably predict the reference infection risk (88% balanced accuracy). We did a pioneering effort to homogenize the methodology to deliver decision support to Italian farmers, by involving plant protection services in the DSS definition, to foster a further adoption of IPM practices.

The Latent Relationship Between Soil Vulnerability to Degradation and Land Fragmentation: A Statistical Analysis of Landscape Metrics in Italy, 1960-2010.

Land degradation leads to almost unpredictable spatial outcomes and environmental dynamics demanding a more integrated monitoring approach. In this framework, we debate on (apparent and latent) connections between land fragmentation and soil degradation by identifying areas with increased levels of soil degradation that underlie distinctive spatial trends of land fragmentation. Moving from land-use maps to an empirical study of desertification, the framework proposed in this work may support environmental monitoring and inform land conservation policies. To assess land fragmentation, a quantitative approach grounded on a comprehensive analysis of landscape metrics available in FRAGSTATS package was illustrated and applied to Italy as a representative case of complex landscape dynamics in the Mediterranean basin. The Environmental Sensitive Area methodology was adopted to monitor the level of soil vulnerability to degradation. Three classes of land vulnerability-unaffected, fragile and critical-were identified and analysed using metrics to investigate possible links between soil degradation and land fragmentation. During the study period (1960-2010), Italy evolved towards a more fragmented landscape, characterised by increasingly smaller and contiguous patches, heterogeneous land-use structures and more irregular patches. We also introduced concepts focusing on syndromes of soil degradation characterised by a variety of attributes that correlate with land fragmentation. The present study makes an important contribution towards an operational system for identifying areas at risk of desertification. Analysis of land fragmentation as a proxy of soil degradation allows the characterisation of general landscape changes and identification of place-specific patterns associated with spatio-temporal dynamics leading to higher risk of desertification.

Complexity in action: Untangling latent relationships between land quality, economic structures and socio-spatial patterns in Italy.

Land quality, a key economic capital supporting local development, is affected by biophysical and anthropogenic factors. Taken as a relevant attribute of economic systems, land quality has shaped the territorial organization of any given region influencing localization of agriculture, industry and settlements. In regions with long-established human-landscape interactions, such as the Mediterranean basin, land quality has determined social disparities and polarization in the use of land, reflecting the action of geographical gradients based on elevation and population density. The present study investigates latent relationships within a large set of indicators profiling local communities and land quality on a fine-grained resolution scale in Italy with the aim to assess the potential impact of land quality on the regional socioeconomic structure. The importance of land quality gradients in the socioeconomic configuration of urban and rural regions was verified analyzing the distribution of 149 socioeconomic and environmental indicators organized in 5 themes and 17 research dimensions. Agriculture, income, education and labour market variables discriminate areas with high land quality from areas with low land quality. While differential land quality in peri-urban areas may reflect conflicts between competing actors, moderate (or low) quality of land in rural districts is associated with depopulation, land abandonment, subsidence agriculture, unemployment and low educational levels. We conclude that the socioeconomic profile of local communities has been influenced by land quality in a different way along urban-rural gradients. Policies integrating environmental and socioeconomic measures are required to consider land quality as a pivotal target for sustainable development. Regional planning will benefit from an in-depth understanding of place-specific relationships between local communities and the environment.

Shaping the role of 'fast' and 'slow' drivers of change in forest-shrubland socio-ecological systems.

The temporal speeds and spatial scales at which ecosystem processes operate are often at odds with the scale and speed at which natural resources such as soil, water and vegetation are managed those. Scale mismatches often occur as a result of the time-lag between policy development, implementation and observable changes in natural capital in particular. In this study, we analyse some of the transformations that can occur in complex forest-shrubland socio-ecological systems undergoing biophysical and socioeconomic change. We use a Multiway Factor Analysis (MFA) applied to a representative set of variables to assess changes in components of natural, economic and social capitals over time. Our results indicate similarities among variables and spatial units (i.e. municipalities) which allows us to rank the variables used to describe the SES according to their rapidity of change. The novelty of the proposed framework lies in the fact that the assessment of rapidity-to-change, based on the MFA, takes into account the multivariate relationships among the system's variables, identifying the net rate of change for the whole system, and the relative impact that individual variables exert on the system itself. The aim of this study was to assess the influence of fast and slow variables on the evolution of socio-economic systems based on simplified multivariate procedures applicable to vastly different socio-economic contexts and conditions. This study also contributes to quantitative analysis methods for long-established socio-ecological systems, which may help in designing more effective, and sustainable land management strategies in environmentally sensitive areas.

Mapping forest fuels through vegetation phenology: the role of coarse-resolution satellite time-series.

Traditionally fuel maps are built in terms of 'fuel types', thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250 m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000-2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies.

Wildfire seasonality and land use: when do wildfires prefer to burn?

Because of the increasing anthropogenic fire activity, understanding the role of land-use in shaping wildfire regimes has become a major concern. In the last decade, an increasing number of studies have been carried out on the relationship between land-use and wildfire patterns, in order to identify land-use types where fire behaves selectively, showing a marked preference (or avoidance) in terms of fire incidence. By contrast, the temporal aspects of the relationship between landuse types and wildfire occurrence have received far less attention. The aim of this paper is, thus, to analyze the temporal patterns of fire occurrence in Sardinia (Italy) during the period 2000-2006 to identify land-use types where wildfires occur earlier or later than expected from a random null model. The study highlighted a close relationship between the timing of fire occurrence and land-cover that is primarily governed by two complementary processes: climatic factors that act indirectly on the timing of wildfires determining the spatial distribution of land-use types, and human population and human pressure that directly influence fire ignition. From a practical viewpoint, understanding the temporal trends of wildfires within the different land-use classes can be an effective decision-support tool for fire agencies in managing fire risk and for producing provisional models of fire behavior under changing climatic scenarios and evolving landscapes.