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The aim of this study is to evaluate human thermal comfort in different green area settings in the city of Florence by using the Universal Thermal Climate Index (UTCI). Field measurements of air temperature, solar radiation, relative humidity, wind speed and black globe thermometer were collected during hot summer days in various parts of Cascine Park, the biggest urban park in Florence (Italy). UTCI was evaluated over different surfaces (asphalt, gravel and grass) completely exposed to the sun or shaded by a large lime tree (Tilia × europaea). The results showed strong differences in UTCI values depending on the exposure to tree shade, while no significant difference was found among ground-cover materials when all surfaces were equally exposed to solar radiation. Future studies are needed to investigate the microclimatic effects of different tree species on UTCI.

Copper (Cu) is among the main contaminant of agricultural soil. The reclamation of Cu polluted soils can be achieved with phytoextraction even if, in general, plants are Cu-excluders and uncommon are Cu-accumulators. The research objectives were to establish the Cu removal capacity by arable and vegetable crops and to investigate the distribution of Cu in their roots, stems and leaves, and fruits. Pot trials were conducted for two subsequent years in Tuscany (Italy). Cu was added into soil in four levels (0, 200, 400, 600 mg kg−1 of Cu). At harvesting, the crops roots, stems and leaves, and fruits or seeds were separately collected, oven dried, weighted, milled and separately analyzed. The results show that the GDUs value to reach the physiological maturity for barley, common bean, Indian mustard, and ricinus was significantly positively correlated with Cu concentration in soil in contrast with observed in sorghum, spinach, and tomato. Leaves and stems of spinach and ricinus have a good storage capacity in contrast with common bean, tomato, Indian mustard sorghum and barley. Tomato storage Cu mainly in fruits and roots which show a remarkable concentration of Cu that increases progressively with the increase of Cu concentration in the soil. In addition, the roots of common bean and ricinus showed a very high concentration of Cu. All species can be considered Cu-excluders because of their low capacity to uptake high quantity of Cu. Indian mustard can be considered a plant able to translocate the metal from root to epigeal tissue.

The resilience of quinoa to drought stress conditions makes the crop suitable for the Sahel region. It can support grain production during the dry season and be considered an alternative crop for alleviating food insecurity within the region. Given the importance of this crop outside the indigenous cultivation area, there is a requisite for the development of crop models to facilitate further expansion of quinoa along the Sahel region. Crop water models are of interest due to increasing pressure on water resources, and the portrayal of irrigation scheduling as the best option for water optimisation. The AquaCrop model was selected, as this model simulates crop development and derives both optimal frequencies and net applications of irrigation. Due to limited water resources in the region, different irrigation regimes [full irrigation, progressive drought (PD), deficit irrigation and extreme deficit irrigation] were proposed for analysing yield and biomass responses to water stress conditions. Results suggest that yields were stabilised at around 1.0 Mg ha–1 under PD, thereby prioritising maximum water productivity rather than maximum yields. Water optimisation was attained by watering less at a suggested 310 mm, but with more frequent irrigation events, 28 rather than 20. Highlights - 13% yield losses when comparing progressive drought with full irrigated treatments. - 25% water savings when comparing progressive drought with full irrigated treatments. - Calibrated and validated NRMSE values of 10.0% and 13.3% for biomass, respectively.- Calibrated and validated NRMSE values of 19.2% and 7.3% for yield, respectively.

A preliminary study was conducted to analyze the sustainability of barley production through: (i) investigating sensor-based nitrogen (N) application on barley performance, compared with conventional N management (CT), (ii) assessing the potential of the Normalized Difference Vegetation Index (NDVI) at different growth stages for within-season predictions of crop parameters, and (iii) evaluating sensor-based fertilization benefits in the form of greenhouse gasses mitigation. Barley was grown under CT, sensor-based management (RF) and with no N fertilization (Control). NDVI measurements and RF fertilization were performed using a GreenSeeker&trade, 505 hand-held optical sensor. Gas emissions were measured using a static chamber method with a portable gas analyzer. Results showed that barley yield was not statistically different under RF and CF, while they both differed significantly from Control. Highly significant positive correlations were observed between NDVI and production parameters at harvesting from the middle of stem elongation to the medium milk stage across treatments. Our findings suggest that RF is able to decrease CO2 emission in comparison with CF. The relationship between N fertilization and CH4 emission showed high variability. These preliminary results provide an indication of the benefits achieved using a simple proximal sensing methodology to support N fertilization.

The Sahel region is considered a hotspot for climate change hazards and vulnerability of weather reliant sectors, including agriculture. Farmers in Burkina Faso have a long history of adapting their farming activities to frequent changes in climate. Using 150 in-person surveys, this study assesses farmers&rsquo, perceptions of climate change based on multiple climate variables, and reviews adaptation practices, including soil and water conservation strategies, in the Soudanian, Soudano-Sahelian, and Sahelian agroclimatic zones of Burkina Faso. In general, farmers are aware of changing climatic conditions, including increased temperatures, greater rainfall variability, heavier precipitation events, delayed onset, and premature offset of the rainy season. However, farmers perceive shifts in climate differently depending on their location and agroclimatic zone. As a result, different adaption strategies are implemented by farmers according to the climatic, societal, and economic context. Survey results show that in the Sahel, climate adaptation strategies rely on traditional knowledge and experimental approaches, whereas in the Soudanian zone, where weather conditions are more favorable for agriculture, adaptation practices are market oriented. These regional differences are important for targeting advisory services, planning processes, and decision-making to support the effective provision of weather and climate information services to the last mile.

Agricultural intensification and soil mismanagement have been recognized among the main causes of soil erosion in Mediterranean climate areas such as the Arbia stream basin (Tuscany, Italy). This study aims at predicting soil loss from agricultural fields as it is essential for providing reliable information for prioritizing soil conservation measures. Thus, measured soil loss from 243 agricultural fields within the Arbia stream basin during the period 2007&ndash, 2010 were used to calibrate and validate the ArcSWAT 2012 model at hydrological response units (HRU) scale. Analysis of variance with post-hoc Tukey honest significant test was used to assess significant measured soil loss differences between slope steepness classes and land covers. Soil loss estimation was always &ldquo, very good&rdquo, for irrigated field crops, olive groves, and vineyards, &ldquo, good&rdquo, for unirrigated field crops, and &ldquo, unsatisfactory&rdquo, for broad-leaved forest. The model succeeded in the quantitative assessment of erosive processes at HRU scales. Its application to the whole Arbia stream basin estimated that 31% of the total surface is subjected to higher erosion levels. This approach might help facilitate the identification of priority areas that need the implementation of conservation measures.

GPS collars for wildlife provide a large amount of spatio-temporal location data and are frequently equipped with sensors that record the animal-level environmental temperature at a schedulable sampling frequency. The simultaneous collection of environmental temperature and animal location may contribute not only to deepen the understanding of animal behavior in different climatic conditions, but also to increase the knowledge of climate features in inaccessible areas. The measurement of environmental temperature provided by the sensors, however, can be biased by several factors (e.g., surface temperature of the animal, direct solar radiation, precipitation), so in-depth studies are required to verify the correlation. The aim of this study was to identify an equation for correcting the collar-recorded temperature data, allowing to improve and refine the results obtained by the analysis of spatial data and to highlight the environmental factors having the greatest impact on the accuracy of the measures. Temperature data from GPS collars were obtained within a research on spatial behavior on 11 hinds while spatialized temperature data were obtained from LAMMA-IBIMET dataset. These data showed high correlation and an identical trend, although the GPS collar temperature data was always higher. This model could represent a tool to obtain an accurate measurement of temperatures in complex geographical areas with wild animals but low density of weather stations. The availability of corrected temperature data, recorded simultaneously with the animal location, could be useful for a more accurate comprehension of animal behavior in free-ranging conditions, both in case of forthcoming studies and to valorize existing datasets.

The spatial prediction of growing stock volume is one of the most frequent application of remote sensing for supporting the sustainable management of forest ecosystems. For such a purpose data from active or passive sensors are used as predictor variables in combination with measures taken in the field in sampling plots. The Sentinel-2 (S2) satellites are equipped with a Multi Spectral Instrument (MSI) capable of acquiring 13 bands in the visible and infrared domains with a spatial resolution varying between 10 and 60 m. The present study aimed at evaluating the performance of the S2-MSI imagery for estimating the growing stock volume of forest ecosystems. To do so we used 240 plots measured in two study areas in Italy. The imputation was carried out with eight k-Nearest Neighbours (k-NN) methods available in the open source YaImpute R package. In order to evaluate the S2-MSI performance we repeated the experimental protocol also with two other sets of images acquired by two well-known satellites equipped with multi spectral instruments: Landsat 8 OLI and RapidEye scanner. We found that S2 worked better than Landsat in 37.5% of the cases and in 62.5% of the cases better than RapidEye. In one study area the best performance was obtained with Landsat OLI (RMSD = 6.84%) and in the other with S2 (RMSD = 22.94%), both with the k-NN system based on a distance matrix calculated with the Random Forest algorithm. The results confirmed that S2 images are suitable for predicting growing stock volume obtaining good performances (average RMSD for both the test areas of less than 19%).

During the past sixty years, the social and economic development causes a change of traditional activities and a movement of the population towards urbanized areas in different regions of Italy. The replacement of vegetated land surface with impervious surface areas changed the hydrologic fluxes of a drainage basin with important consequences on the basin resilience to rainfall events. The main objective of this paper is to present the methodology and the results of a study for quantifying and analysing the hydrological responses to land use and climate changes by means of ArcSWAT model. The case study was the runoff dynamic of the Elsa river basin which was analysed by climate and land use change along more than fifty years (1954–2007). Results showed that, the model was effective in simulating daily base, peak and total runoff, scoring a very good performance. Analysis indicated that given the same climatic conditions, changes in land use played a considerable role since in peak and total runoff formation that were increased with the increase of artificial surfaces and specialised agriculture. This type of analysis proved to be effective in analysing past and future hydrological dynamics of the basin. Our findings suggest that such an approach could help policymakers involved in the land use planning in taking into account land use change, since it can amplify the effects of climate and demographic changes on basin hydrodynamics.

Soil erosion is among the major processes that threaten agricultural soils, causing soil and organic matter losses, loss of inherent fertility, and water contamination. In this context, vineyards are the most erosion-prone lands in the Mediterranean region. The adoption of alternative soil management practices, such as the use of permanent grass, can reduce runoff and erosion. Based on an 8-years field study in Tuscany (Central Italy), this research aimed to assess the long-term consequences of permanent grass cover in vineyard plots in terms of both runoff and soil and nutrient losses, and to investigate their relationships with rainfall characteristics. The results showed that permanent grass cover reduced significantly the average yearly runoff (by about 11.7%), in particular during copious and intense rainfalls. Also, grass covered parcels lost on average 68.5% less soil than the inter-rows with harrowed soil. Up to 4.5 and 12.5 kg ha −1 year −1 of N and up to 6.2 and 5 kg ha −1 year −1 of P were lost in grassed and harrowed parcels, respectively. Under both soil management practices, the total soil and nutrient losses caused by runoff were significantly correlated with rainfall height and rainfall erosivity.

Leaf wetness (LW) is a key environmental variable for the development of foliar fungal diseases of citrus. However, little information of LW duration (LWD) in Mediterranean citrus-growing areas is available. LWD in six canopy positions and two leaf sides was studied with visual observations and Spectrum LW sensors in a citrus orchard in Spain. The performance of Spectrum and Hobo LW sensors, and CART/SLD and RH LW models was assessed by receiver operating characteristic (ROC) curve analysis. The dynamics of LWD in three citrus orchards with flood irrigation and three with drip irrigation were evaluated using time-series models. LWD data from all experiments and recordings in 14 commercial citrus orchards illustrated the high frequency of LW periods, particularly those associated with dew. No substantial differences in LWD were detected among citrus canopy positions and leaf sides. An optimal dry/wet threshold of 2.85 for the Spectrum LW sensor, 75.88 for the Hobo LW sensor, 15.99 for the CART/SLD model, and 87.45% for the RH model was selected by ROC curve analysis. All the sensors and models evaluated showed a substantial strength of agreement with visual observations of LW, with area under the ROC curve of 0.89–0.93 and Cohen’s Kappa index of 0.61–0.77. The RH model was recommended because of easier implementation in citrus orchards in Spain. Time-series models did not detect any significant increase in LWD associated with flood irrigation, but only after some rain events. High mesoclimatic relative humidity in the study area outweighed possible microclimatic changes induced by flood irrigation. Thus, negligible effects of flood irrigation in the infection of foliar fungal pathogens would be expected, but longer LWD in the lower canopy as well as potential effects on inoculum dynamics in the leaf litter cannot be excluded.

In recent years digital sensors have been successfully integrated on board Unmanned Aerial Vehicles (UAV) to assess crop vigour, vegetation coverage, and to quantify the ‘greenness’ of foliage as indirect measurements of crop nitrogen status. The classical approach of precision agriculture has involved the use of multispectral sensors onboard UAV and the development of numerous vegetation indices associated with vegetation parameters, such as the mostly used Normalized Difference Vegetation Index (NDVI). However, the main negative issue when dealing with multi and hyperspectral reflectance measuring tools is their high cost and complexity from the operational point of view. As a low-cost alternative, vegetation indices derived from Red Green Blue (RGB) cameras have been employed for remote-sensing assessment, providing data on different stress conditions and species. Digital images record information as amounts of RGB light emitted for each pixel of the image; however, the intensity of red and blue will often alter how green an image appears. To simplify the interpretation of digital colour data, recent studies have suggested converting RGB values to the more intuitive Hue, Saturation, and Brightness (HSB) colour spectrum, and then into a single measure of dark green colour, the Dark Green Color Index (DGCI). In this study, NDVI acquired by a ground-based handheld crop sensor and by a multispectral camera mounted on board a UAV has been compared with DGCI calculated from images taken with a commercial digital camera on board a UAV, trying to quantify the colour of turfgrass that had received different nitrogen (N) rates. The objectives of the trial were to study an affordable easy-to-use tool evaluating the relationship among NDVI, DGCI and leaf nitrogen content on turfgrass.

Agricultural land set up systems comprise those agronomic structures able to preserve the soil fertility from water erosion, such as: ditching, contouring, earth-riser and stonewall terracing, draining, and channelization, etc.. However, in the past 60 years, agricultural mechanization has led to an expansion of the field size and reduction in land set up system intensity to make machine operation more feasible and cheaper. As a consequence, these transformations have made sloping fields less resilient to the storms and accelerated the soil erosion processes. Based on an 8- year field study in ‘Chianti Classico’ area (Tuscany, Central Italy), this research aimed to evaluate the effectiveness of the land set up systems such as diversion ditch, earth-riser and stonewall terracing on reducing water erosion from field crops, olive orchards, and vineyards. The results showed that diversion ditches were effective on herbaceous crop fields with slope steepness lower than 9%. While, for higher slopes, diversion ditches were not sufficient to contain the soil loss within OECD 2008 tolerable limits in none of the considered land uses. On the opposite, in steep slopes, earth-riser terraces and stonewall terraces have shown their value as land set up system capable of reducing the erosive process. Their greatest drawback is the reduction of the cultivable surface deriving from the presence of the riser and the walls. However, their added value as a precious element characterizing the local landscape was of considerable importance for the local economy linked to tourism. Highlights - Diversion ditches reduce soil erosion on herbaceous crop fields with slope lower than 9%. - Diversion ditches did not contain the soil loss within acceptable limits on steep slopes. - Earth-riser terraces and stonewall terraces reduced soil losses within acceptable limits. - Terraces reduced soil loss by 4.7-12.3 times with respect to diversion ditches. - The analysis was performed on measured average annual soil loss data from 695 fields.

Interactions between model parameters and low spatiotemporal resolution of available data mean that conventional soil organic carbon (SOC) models are often affected by equifinality, with consequent uncertainty in SOC forecasts. Estimation of belowground C inputs is another major source of uncertainty in SOC modelling. Models are usually calibrated on SOC stocks and fluxes from long‐term experiments (LTEs), whereas other point data are not used for constraining the model parameters. We used data from an agricultural long‐term (>65 years) fertilization experiment to test a multi‐objective parameter estimation approach on the RothC model, combining SOC data from different fertilization treatments with microbial biomass, basal respiration and Zimmermann's fractions data. We also compared two methods to estimate the belowground C inputs: a conventional scaling of belowground biomass from crop harvest yield and an alternative approach based on constant belowground C for cereals measured experimentally in the field. The resulting posterior parameter distributions still suffered from some equifinality; the most stable C pool kinetic constants and composition of exogenous organic matter were the most sensitive parameters. The use of fixed belowground C inputs for cereals improved the model performance, reducing the importance of treatment‐specific parameters and processes. The introduction of microbial biomass and basal respiration data was effective for increasing determination of the calibration, but also suggested a change in the model structure: the microbial biomass pool, which is proportional to the C inputs in the traditional models, could be represented by different microbial physiology functions. HIGHLIGHTS: Multi‐objective calibration with SOC and microbial or soil fractionation data can reduce model uncertainty. We compared different methods to estimate belowground C inputs on a long‐term trial in Switzerland. Fixed belowground C inputs measured in the field gave the best model performance. Microbial data can improve model calibration, but a change in model structure is suggested.

The issue of soil erosion is considered highly important by local administrators of the Chianti region. Thus, a methodology for predicting the long-term average annual soil loss, by using the Revised Universal Soil Loss Equation (RUSLE) in a Geographical Information System framework was developed and assessed. The rainfall and runoff erosivity factor was calculated using 35 raingauges with an acquisition interval of 15 min in the period from 1996 to 2010. The soil erodibility factor was estimated using a soil map at a scale of 1:50,000. The topographic factor was calculated from a 10 m digital elevation model. Moreover, a methodology was proposed that took into account the vineyard row direction and slope. Soil loss field data (566 field sites) were measured over 6 years using a topographical approach, and used to validate the model results. The statistical indices for the evaluation of the model were, respectively, the mean percent error (M%E), − 0.1%, the ratio of the RMSE to the standard deviation of the observations (RSR), 23.7%; and the Nash–Sutcliffe coefficient (NCS), 0.9. Statistics indicated good predictions of long-term average annual soil losses at a field scale. The average annual soil loss for the study area was predicted up to 6.4 t·ha − 1 ·y − 1 . Approximately 13% of the study area was classified as high erosion (≥ 22 t·ha − 1 ·y − 1 ). The identification of areas with the greatest erosion risk supports the possibility of using the model for land-use and land-management planning purposes. Moreover, this assists in the identification of those areas towards which conservation measures can be directed.

Zinc (Zn) is naturally present in soils and constitutes an essential micronutrient for plants. Mining, industrial, as well as various agricultural activities all contribute to increasing the Zn concentrations in soils to levels that are toxic for plants. The aim of this study was to evaluate the capacity of field crops to remove Zn from contaminated soils. The experimental design included 28 treatments, comprising seven field crops (Hordeum vulgare L., Ricinus communis L., Phaseolus vulgaris L., Brassica juncea Czem., Sorgum vulgare L., Spinacea oleracea L., Solanum lycopersicum L.) and four Zn levels (0, 500, 1000, 1500 mg kg−1) applied to soils. The dry weight (DW) of the aboveground biomass of R. communis and S. lycopersicum increased significantly as the Zn concentration in the soil increased, whereas the DW significantly decreased in P. vulgaris, B. juncea and S. vulgare. Results indicated that S. oleracea was the most efficient in concentrating Zn in the aboveground tissues, followed in decreasing order by H. vulgare, S. lycopersicum, R. communis, S. vulgare, P. vulgaris, and B. juncea. H. vulgare resulted the most efficient in accumulating Zn both in fruit and in leaves and stems, whereas S. lycopersicum resulted the most efficient in accumulating Zn in roots. The BAF and TF values indicated that H. vulgare and S. oleracea resulted being suitable for Zn phytoextraction, whereas the remaining crops being suitable for Zn phytostabilization. These results highlight the phytoremediation potential of the seven analysed crops.

A research study was conducted in an open field tomato crop in order to: (i) Evaluate the capability of Sentinel-2 imagery to assess tomato canopy growth and its crop water requirements, and (ii) explore the possibility to predict crop water requirements by assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model. The pilot area was in Campania, a region in the south west of Italy, characterized by a typical Mediterranean climate, where field campaigns were conducted in seasons 2017 and 2018 on processing tomato. Crop water use and irrigation requirement were estimated by means of three different methods: (i) The AquaCrop model, (ii) an irrigation advisory service based on Sentinel-2 imagery known as IRRISAT and (iii) assimilating the canopy cover estimated by Sentinel-2 imagery into AquaCrop model Sentinel-2 imagery proved to be effective for monitoring canopy growth and for predicting irrigation water requirements during mid-season stage of the crop, when the canopy is fully developed. Conversely, the integration of the Sentinel-2 imagery with a crop growth model can contribute to improve the irrigation water requirement predictions in the early and development stage of the crop, when the soil evaporation is not negligible with respect to the total evapotranspiration.

Climate change will increase the frequency and severity of hazard events such as heat waves, with important effects in several European regions. It is of importance to consider overall effects as well as specific impact on vulnerable population groups such as outdoor workers. The agricultural and construction sectors represent two strategic occupational fields that in relatively recent years involve an increasing number of migrant workers, and therefore require a better management of cultural aspects, that may interact with and impact on heat-related health risk. For this reason, the present study evaluated heat-stress perception and management among native and immigrant workers in Europe. As part of the EU&rsquo, s Horizon 2020 HEAT-SHIELD project (grant agreement No. 668786), two agricultural and one construction companies, traditionally employing migrant workers, were evaluated with a questionnaire survey during the summer months of 2017. The data collected (104 case studies) were analyzed using descriptive statistics (Chi-squared tests) and the analysis of variance was performed with ANOVA test. From the results, migrant workers declared that work required greater effort than do native Italian workers (&chi, 2 = 17.1, p = 0.001) but reported less impact from heat on productivity (&chi, 2 = 10.6, p = 0.014) and thermal discomfort. In addition, migrant workers were mainly informed through written or oral communications, while native workers received information on heat-health issues through training courses. These findings are of importance for future information and mitigation actions to address socio-cultural gaps and reduce heat-stress vulnerability.

Digestate is considered a sustainable opportunity to reduce environmental impact from fertilization, due to high content of nitrogen easily available for plants and for the low impact of its production. We tested liquid fraction of digestate from anaerobic digestion of pig slurries and urea, to assess the emissions of nitrous oxide and ammonia from soil on silage maize (Zea mays L.). Nitrogen rate was the same for both treatments (150 kg/ha) spread replacing common methods. Emissions measurements were performed immediately after fertilization using a static chamber method with a portable gas analyser. Measurements were performed daily during the first week, and twice per week until no emissions from the soil were observed. Cumulative nitrogen emissions show that digestate can be an efficient method to reduce nitrogen losses (2.87 kg N/ha/25 days and 3.76 kg N/ha/25 days for digestate and urea respectively). However, the two fertilizers emitted different kind of gases: compared to urea, digestate emitted the 23% of nitrous oxide more, on the other hand urea emitted 66% of ammonia more than digestate. Crop yield obtained under the two fertilization methods did not significantly differ in terms of dry matter (DM) (13.63 t DM/ha and 13.24 t DM/ha for digestate and urea, respectively) (significance factor α > 0.5).

In recent years, the certification of environmental sustainability has been adopted by a large number of farms. A wide range of recent literature proved consumers’ preference and willingness to pay (WTP) for certification claiming for reduced environmental impact of food production, whereas the literature on farmers’ preference for a specific scheme design is scant. This study aims at investigating the possibilities of developing an environmental certification (EC) for agricultural products that is more tailored to farmers’ expectations. Data from an original survey among 116 producers from Italy, Croatia, and Greece were used to investigate the most preferred elements of a hypothetical EC for a general agricultural product, by means of a choice experiment. Although differences emerge in relation to the nationality of respondents, the results on average suggest a clear preference and a higher WTP by farmers for a certification that may guarantee an efficient use of water resources. Furthermore, farmers are found to be more inclined toward a public certifying body and the possibility to receive technical assistance for the scheme adoption.

In a context of urban warming, the effects of trees on outdoor thermal stress are important even during the increasingly hot autumn season. This study examines the effects of a deciduous tree species (Tilia x europaea L) on surface temperature over different ground materials and in turn on human thermal comfort, with a particular focus on tree shade variation due to leaf fall. Grass, asphalt, and gravel-covered ground surfaces, both sun-exposed and under the Tilia, were monitored in Florence, Italy, during the summer (2014) and autumn (2017) seasons. The Index of Thermal Stress (ITS) was used to gauge the micrometeorological effects of the changing tree canopy, with tree defoliation quantified by the Plant Area Index. On clear summer days, thermal discomfort was especially pronounced over exposed asphalt, and even more intense above exposed gravel due to its higher reflectivity—while shaded surfaces showed large reductions in thermal stress. Even though incoming solar radiation decreases over the course of the fall season, the direct radiation under the gradually defoliating tree canopy actually increases. Due to this diminished shading effect, the differences in surface temperature between exposed and shaded asphalt shrink dramatically from about 20 to 3 °C. However, since ambient conditions become milder as the season progresses, the Tilia demonstrated a double benefit in terms of ITS: providing thermal comfort under its full canopy at the beginning of autumn and maintaining comfort even as its canopy thins out. At the same time, tree species with earlier defoliation may be unable to replicate such benefits.

The urban population growth, together with the contemporary deindustrialization of metropolitan areas, has resulted in a large amount of available land with new possible uses. It is well known that urban green areas provide several benefits in the surrounding environment, such as the improvement of thermal comfort conditions for the population during summer heat waves. The purpose of this study is to provide useful information on thermal regimes of urban soils to urban planners to be used during an urban transformation to mitigate surface temperatures and improve human thermal comfort. Field measurements of solar radiation, surface temperature (T-s), air temperature (T-air), relative humidity, and wind speed were collected on four types of urban soils and pavements in the city of Florence during summer 2014. Analysis of days under calm, clear-sky condition is reported. During daytime, sun-to-shadow differences for T-air, apparent temperature index (ATI), and T-s were significantly positive for all surfaces. Conversely, during nighttime, T-air differences among all surfaces were significantly negative, whereas ATI showed significantly positive differences. Moreover, T-s was significantly negative for grass and gravel. Relative to the shaded surfaces, T-s was higher on white gravel and grass than gray sandstone and asphalt during nighttime, whereas gray sandstone was always the warmest surface during daytime. Conversely, no differences were found during nighttime for ATI and T-air measured over surfaces that were exposed to sun during the day, whereas T-s showed higher values on gravel than grass and asphalt during nighttime. An exposed surface warms less if its albedo is high, leading to a significant reduction of T-s during daytime. These results underline the importance of considering the effects of surface characteristics on surface temperature and thermal comfort. This would be fundamental for addressing urban environment issues toward the heat island mitigation considering also the impact of urban renovation on microclimate.

Greenhouse gas (GHGs) emissions into the atmosphere derived from the use of fertilisers is a serious issue for the sustainability of agricultural systems, also considering that the growing global demand for food requires an increasingly productive agriculture. Emissions dynamics are very variable and are determined by many factors and their reciprocal interactions. Among driving factors, soil type (mineral, organic and microbiological composition), fertilisation method, climate, and the cropping system. In the present experiment, the combined effect of soil organic matter (SOM) and fertilisation method on the emissions of GHGs and ammonia (NH3) was investigated. Liquid fraction of digestate from pig slurries, compost from organic fraction of municipal solid wastes, and urea were applied on bare soil with two levels of organic matter (OM1: 1.3% and OM2: 4.3%). Emissions were directly monitored by a static chamber system and a portable gas analyser. Results show that soil organic matter as well as the composition of the fertilisers affect greenhouse gasses emissions. Emissions of methane (CH4) produced by digestate and compost during experimental period were higher in correspondence of lower organic matter content (0.58-0.49 kg CH4 C/ha/day and 0.37-0.32 kg CH4 C/ha/day for digestate and compost respectively), contrary to what was observed for urea. For all fertilisers, carbon dioxide (CO2) and nitrous oxide (N2O) emissions were higher in correspondence of higher organic matter level. In particular, CO2 emissions were 11.05%, 67.48% and 82.84% higher in OM2 than OM1 for digestate, urea and compost respectively. Likewise, N2O emissions were 87.45%, 68.97% and 92.11% higher in OM2 than OM1 for digestate, urea and compost respectively. The obtained results show that the content of organic matter in soils plays a key role on the emissions of GHGs, generally enhancing the levels of gas emissions.

Rapid and unplanned urban growth is responsible for the continuous conversion of green or generally natural spaces into artificial surfaces. The high degree of imperviousness modifies the urban microclimate and no studies have quantified its influence on the surface temperature (ST) nearby residential building. This topic represents the aim of this study carried out during summer in different urban zones (densely urbanized or park/rural areas) of Parma (Northern Italy). Daytime and nighttime ASTER images, the local urban cartography and the Italian imperviousness databases were used. A reproducible/replicable framework was implemented named Building Thermal Functional Area (BTFA) useful to lead building-proxy thermal analyses by using remote sensing data. For each residential building (n = 8898), the BTFA was assessed and the correspondent ASTER-LST value (ST_BTFA) and the imperviousness density were calculated. Both daytime and nighttime ST_BTFA significantly (p < 0.001) increased when high levels of imperviousness density surrounded the residential buildings. These relationships were mostly consistent during daytime and in densely urbanized areas. ST_BTFA differences between urban and park/rural areas were higher during nighttime (above 1 degrees C) than daytime (about 0.5 degrees C). These results could help to identify urban thermal Hot-Spots that would benefit most from mitigation actions.

Heatwaves (HWs) are one of the “natural” hazards with the greatest impact worldwide in terms of mortality and economic losses, and their effects may be exacerbated in large urban areas. For these reasons, more detailed analyses of urban HW trends represent a priority that cannot be neglected. In this study, HW trends were investigated during the warmest period of the year (May–September) by using a slightly improved version of the EuroHEAT HW definition applied on long meteorological time-series (36-year period, 1980–2015) collected by weather stations located in the capitals of the 28 European Union member countries. Comparisons between two 18-year sub-periods (1980–1997 vs. 1998–2015) were carried out and a city-specific HW hazard index (HWHI), accounting for the main HW characteristics, was proposed. Most of the capitals revealed significant positive trends of the majority of HW hazard characteristics and substantial HWHI increases were observed during the sub-period 1998–2015, especially in the central-eastern and southeastern cities. Conversely, minor HWHI increases were observed in most of the northern capitals and opposite situations were even observed in several northern and especially southwestern cities. The results of this study represent a support for planning urban HW-related mitigation and adaptation strategies with the priority given to the southeastern cities.

Worldwide, glyphosate is the most widely used herbicide in controlling the growth of annual and perennial weeds. An increasing number of studies have highlighted the environmental risk resulting from the use of this molecule in aquatic and terrestrial ecosystems. The objective of the study was to determine the transport of glyphosate and its degradation product, aminomethylphosphonic acid (AMPA), through runoff and transported sediment from a vineyard under two different soil management systems: harrowed inter-row (HR) and permanent grass covered inter-row (GR). The study was performed over a period of 4 yr. Glyphosate and AMPA concentrations were found to be higher in runoff and in transported sediment from HR compared with GR, regardless of the amount of runoff and transported sediment. The mean annual percentages of glyphosate loss, via runoff and transported sediment, were about 1.37 and 0.73% for HR and GR, respectively. Aminomethylphosphonic acid represented approximately 30.9 and 40.0% of the total glyphosate losses in GR and HR, respectively. Moreover, results suggested that rains occurring within 4 wk after treatment could cause the transport of glyphosate and AMPA in high concentrations. Soil analyses indicated that glyphosate content was below detection within 1 yr, whereas AMPA remained in the soil profiles along the vine row and in the inter-row. Results indicated that GR can reduce soil and herbicide loss by runoff in vineyard cropping system.

To date, projections of European crop yields under climate change have been based almost entirely on the outputs of crop-growth models. While this strategy can provide good estimates of the effects of climatic factors, soil conditions and management on crop yield, these models usually do not capture all of the important aspects related to crop management, or the relevant environmental factors. Moreover, crop-simulation studies often have severe limitations with respect to the number of crops covered or the spatial extent. The present study, based on agroclimatic indices, provides a general picture of agroclimatic conditions in western and central Europe (study area lays between 8.5°W–27°E and 37–63.5°N), which allows for a more general assessment of climate-change impacts. The results obtained from the analysis of data from 86 different sites were clustered according to an environmental stratification of Europe. The analysis was carried for the baseline (1971–2000) and future climate conditions (time horizons of 2030, 2050 and with a global temperature increase of 5 °C) based on outputs of three global circulation models. For many environmental zones, there were clear signs of deteriorating agroclimatic condition in terms of increased drought stress and shortening of the active growing season, which in some regions become increasingly squeezed between a cold winter and a hot summer. For most zones the projections show a marked need for adaptive measures to either increase soil water availability or drought resistance of crops. This study concludes that rainfed agriculture is likely to face more climate-related risks, although the analyzed agroclimatic indicators will probably remain at a level that should permit rainfed production. However, results suggests that there is a risk of increasing number of extremely unfavorable years in many climate zones, which might result in higher interannual yield variability and constitute a challenge for proper crop management.

Europe is, after Asia, the second largest producer of wheat in the world, and provides the largest share of barley. Wheat (and to a similar extent, barley) production in Europe increased by more than 6-fold during the 20th century. During the first half of the 20th century, this was driven by expanding the harvested area. This was followed, from the mid-20th century, by a mas- sive increase in productivity that in many regions has stalled since 2000. However, it remains unclear what role climatic factors have played in these changes. Understanding the net impact of climatic trends over the past century would also aid in our understanding of the potential impact of future climate changes and in assessments of the potential for adaptation across Europe. In this study, we compiled information from several sources on winter wheat and spring barley yields and climatological data from 12 countries/regions covering the period from 1901−2012. The studied area includes the majority of climatic regions in which wheat and barley are grown (from central Italy to Finland). We hypothesized that changes in climatic conditions have led to measurable shifts in climate−yield relationships over the past 112 yr, and that presently grown wheat and bar- ley show a more pronounced response to adverse weather conditions compared to crops from the early 20 th century. The results confirm that climate−yield relationships have changed significantly over the period studied, and that in some regions, different predictors have had a greater effect on yields in recent times (between 1991 and 2012) than in previous decades. It is likely that changes in the climate−yield relationship at the local level might be more pronounced than those across the relatively large regions used in this study, as the latter represents aggregations of yields from various agroclimatic and pedoclimatic conditions that may show opposing trends (1) (PDF) Changing regional weather-crop yield relationships across Europe between 1901 and 2012. Available from: https://www.researchgate.net/publication/307899712_Changing_regional_weather-crop_yield_relationships_across_Europe_between_1901_and_2012 [accessed Nov 10 2018].

The IC-FAR national project (Linking long term observatories with crop system modelling for better understanding of climate change impact, and adaptation strategies for Italian cropping systems) initiated in 2013 with the primary aim of implementing data from 16 long term Italian agronomic experiments in a common, interoperable structure. The building of a common database (DB) structure demands a harmonization process aimed at standardising concepts, language and data in order to make them clear, and has to produce a well-documented and easily available tool for the whole scientific community. The Agricultural Model Intercomparison and Improvement Project (AgMIP) has made a great effort in this sense, improving the vocabulary developed by the International Consortium for Agricultural Systems Applications (ICASA) and defining harmonization procedures. Nowadays, these ones have also to be addressed to facilitate the extraction of input files for crop model simulations. Substantially, two alternative directions can be pursued: adapting data to models, building a standard storage structure and using translators that convert DB information to model input files; or adapting models to data, using the same storage structure for feeding modelling solutions constituted by combining model components, re-implemented in the same model platform. The ICFAR information management system simplifies data entry, improves model input extraction (implementing System Dynamics ontology), and satisfies both the paradigms. This has required the development of different software tools: ICFAR-DB for data entry and storage; a model input extractor for feeding the crop models (MoLInEx); SEMoLa platform for building modelling solutions and performing via scripts the model intercomparison. The use of the standard AgMIP/ICASA nomenclature in the ICFAR-DB and the opportunity to create files with MoLInex for feeding AgMIP model translators allow full system interoperability.

Urban areas are characterized by the very high degree of soil sealing and continuous built-up areas: Italy is one of the European countries with the highest artificial land cover rate, which causes a substantial spatial variation in the land surface temperature (LST), modifying the urban microclimate and contributing to the urban heat island effect. Nevertheless, quantitative data regarding the contribution of different densities of built-up surfaces in determining urban spatial LST changes is currently lacking in Italy. This study, which aimed to provide clear and quantitative city-specific information on annual and seasonal spatial LST modifications resulting from increased urban built-up coverage, was conducted generally throughout the whole year, and specifically in two different periods (cool/cold and warm/hot periods). Four cities (Milan, Rome, Bologna and Florence) were included in the study. The LST layer and the built-up-surface indicator were obtained via use of MODIS remote sensing data products (1km) and a very high-resolution map (5m) of built-up surfaces recently developed by the Italian National Institute for Environmental Protection and Research. The relationships between the dependent (mean daily, daytime and nighttime LST values) and independent (built-up surfaces) variables were investigated through linear regression analyses, and comprehensive built-up-surface-related LST maps were also developed. Statistically significant linear relationships (p

The aim of this study is to identify the most effective thermal predictor of heat-related very-elderly mortality in two cities located in different geographical contexts of central Italy. We tested the hypothesis that use of the state-of-the-art rational thermal indices, the Universal Thermal Climate Index (UTCI), might provide an improvement in predicting heat-related mortality with respect to other predictors. Data regarding very elderly people (>=75 years) who died in inland and coastal cities from 2006 to 2008 (May-October) and meteorological and air pollution were obtained from the regional mortality and environmental archives. Rational (UTCI) and direct thermal indices represented by a set of bivariate/multivariate apparent temperature indices were assessed. Correlation analyses and generalized additive models were applied. The Akaike weights were used for the best model selection. Direct multivariate indices showed the highest correlations with UTCI and were also selected as the best thermal predictors of heat-related mortality for both inland and coastal cities. Conversely, the UTCI was never identified as the best thermal predictor. The use of direct multivariate indices, which also account for the extra effect of wind speed and/or solar radiation, revealed the best fitting with all-cause, very-elderly mortality attributable to heat stress. © 2014 Marco Morabito et al.

We aimed to characterise the coincidence of yield variations with weather variables for major field crops using long-term datasets and reveal whether there are commonalities across the European agricultural regions. Long-term national and/or regional yield datasets were used from 14 European countries (total of 25 regions). Crops studied were spring and winter barley and wheat, winter oilseed rape, potato and sugar beet. Relative yield deviations were determined for all crops. Meteorological data on monthly means for temperature variables, solar radiation, accumulated precipitation and evapotranspiration were provided for the relevant agricultural regions of each country for 1975-2008. Harmful effects of high precipitation during grain-filling in grain and seed crops and at flowering in oilseed rape were recorded. In potato reduced precipitation at tuber formation was associated with yield penalties. Elevated temperatures had harmful effects for cereals and rapeseed yields. © 2010 Elsevier B.V. All rights reserved., JRC.H.4-Monitoring Agricultural Resources

Particularly in summer, thermal conditions in urban areas are influenced by solar radiation and human health can be strongly affected by the higher temperature regime increased by the Urban Heat Island effect (UHI). Many studies have been carried out to estimate the temperature distribution in urban areas and some of these use or are based on data collected by meteorological instruments placed within the cities. At microscale, temperature collected by sensors can be influenced by the underlying surface characteristics and the closeness to warm surfaces. The aim of this study is to investigate how different exposure to solar radiation can affect air temperature measurement in streets and gardens. The study was carried out on two different areas in Florence during summer 2007. Shielded air temperature sensors were placed in a street of a high density built-up area and in a green area. Each area was monitored by two sensors, sited in different solar radiation exposure: one in a sunny area and the other in a shaded one. A preliminary data analysis showed a difference in every site between the air temperature values collected by the two sensors especially from the morning to the afternoon. The relationship between air temperature differences and synoptic meteorological conditions were also analyzed. In conclusion, the solar radiation exposure of a monitoring station is an important parameter that must be considered both during the instruments siting and the analysis of data collected by sensors previously placed. The result of this study shows that during particular synoptic conditions, data collected by the two sensors of the same area can be different.

BACKGROUND: Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks. OBJECTIVES: Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥65). METHODS: A long time-series (2001-2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI). RESULTS: The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities. CONCLUSIONS: This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies. Language: en

Existing heat&ndash, health warning systems focus on warning vulnerable groups in order to reduce mortality. However, human health and performance are affected at much lower environmental heat strain levels than those directly associated with higher mortality. Moreover, workers are at elevated health risks when exposed to prolonged heat. This study describes the multilingual &ldquo, HEAT-SHIELD occupational warning system&rdquo, platform (https://heatshield.zonalab.it/) operating for Europe and developed within the framework of the HEAT-SHIELD project. This system is based on probabilistic medium-range forecasts calibrated on approximately 1800 meteorological stations in Europe and provides the ensemble forecast of the daily maximum heat stress. The platform provides a non-customized output represented by a map showing the weekly maximum probability of exceeding a specific heat stress condition, for each of the four upcoming weeks. Customized output allows the forecast of the personalized local heat-stress-risk based on workers&rsquo, physical, clothing and behavioral characteristics and the work environment (outdoors in the sun or shade), also taking into account heat acclimatization. Personal daily heat stress risk levels and behavioral suggestions (hydration and work breaks recommended) to be taken into consideration in the short term (5 days) are provided together with long-term heat risk forecasts (up to 46 days), all which are useful for planning work activities. The HEAT-SHIELD platform provides adaptation strategies for &ldquo, managing&rdquo, the impact of global warming.