Bluegreen Strategy (Italy)

ABSTRACT We present 19 months of continuous monitored flight in a combined data set collected through a multi-sensor data logger attached to a single subadult Common Swift. Similarities in the migration routes, stopover locations visited and flight altitudes between subadult and adult flight patterns, in both migration and overall non-breeding moonlight-driven flight strategy, may be the result of a genetically fixed trait or a learned experience during the relatively long subadult stage of a long-lived species. Deviations from moonlight-driven flight patterns during the breeding season highlight a potential response to anthropogenic skyglow. Our findings refine research avenues with respect to the behaviour of subadult Swifts in relation to migration strategies and the potential influence of skyglow on foraging.

<strong class="journal-contentHeaderColor">Abstract.</strong> Vegetation phenology plays a significant role in driving seasonal patterns in land-atmosphere interactions and ecosystem productivity, and is a key factor to consider when modeling or investigating ecological and land-surface dynamics. To integrate phenology in ecological research ultimately requires the application of carefully curated and quality controlled phenological datasets that span multiple years and include a wide range of different ecosystems and plant functional types. By using digital cameras to record images of plant canopies every 30 minutes, pixel-level information from the visible red-green-blue color channels can be quantified to evaluate canopy greenness (defined as the green chromatic coordinate, <em>G_CC</em>), and how it varies in space and time. These phenological cameras (i.e., &ldquo;PhenoCams&rdquo;) offer a pragmatic and effective way to measure and provide phenology data for both research and education. Here, in this dataset descriptor, we present the PhenoCam dataset version 3 (V3.0), providing significant updates relative to prior releases. PhenoCam V3.0 includes 738 unique sites and a total of 4805.5 site years, a 170 % increase relative to PhenoCam V2.0 (1783 site years), with notable expansion of network coverage for evergreen broadleaf forests, understory vegetation, grasslands, wetlands, and agricultural systems. Furthermore, in this updated release, we now include a PhenoCam-based estimate of the normalized difference vegetation index (<em>cameraNDVI</em>), calculated from back-to-back visible and visible+near-infrared images acquired from approximately 75 % of cameras in the network, which utilize a sliding infrared cut filter. Both <em>G_CC</em> and <em>cameraNDVI</em> showed similar, but somewhat unique, patterns in canopy greenness and VIS vs. NIR reflectance, across various ecosystems, indicating their consistent ability to record phenological variability. However, we did find that at most sites, <em>G_CC</em> time series had less variability and fewer outliers, representing a smoother signal of canopy greenness and phenology. Overall, PhenoCam greenness as measured by both <em>G_CC</em> and <em>cameraNDVI</em> provides expanded opportunities for studying phenology and tracking ecological changes, with potential applications to the evaluation of satellite data products, earth system and ecosystem modeling, and understanding phenologically mediated ecosystem processes. The PhenoCam V3.0 data release is publicly available for download from the Oak Ridge National Lab Distributed Active Archive Center: the source imagery used to derive phenology information is available at <a href="https://doi.org/10.3334/ORNLDAAC/2364" target="_blank" rel="noopener">https://doi.org/10.3334/ORNLDAAC/2364</a> (Ballou et al., 2025), and the summarized phenology data are available at <a href="https://doi.org/10.3334/ORNLDAAC/2389" target="_blank" rel="noopener">https://doi.org/10.3334/ORNLDAAC/2389</a> (Zimmerman et al., 2025).

<strong class="journal-contentHeaderColor">Abstract.</strong> Vegetation phenology plays a significant role in driving seasonal patterns in land-atmosphere interactions and ecosystem productivity, and is a key factor to consider when modeling or investigating ecological and land-surface dynamics. To integrate phenology in ecological research ultimately requires the application of carefully curated and quality controlled phenological datasets that span multiple years and include a wide range of different ecosystems and plant functional types. By using digital cameras to record images of plant canopies every 30 minutes, pixel-level information from the visible red-green-blue color channels can be quantified to evaluate canopy greenness (defined as the green chromatic coordinate, <em>G_CC</em>), and how it varies in space and time. These phenological cameras (i.e., &ldquo;PhenoCams&rdquo;) offer a pragmatic and effective way to measure and provide phenology data for both research and education. Here, in this dataset descriptor, we present the PhenoCam dataset version 3 (V3.0), providing significant updates relative to prior releases. PhenoCam V3.0 includes 738 unique sites and a total of 4805.5 site years, a 170 % increase relative to PhenoCam V2.0 (1783 site years), with notable expansion of network coverage for evergreen broadleaf forests, understory vegetation, grasslands, wetlands, and agricultural systems. Furthermore, in this updated release, we now include a PhenoCam-based estimate of the normalized difference vegetation index (<em>cameraNDVI</em>), calculated from back-to-back visible and visible+near-infrared images acquired from approximately 75 % of cameras in the network, which utilize a sliding infrared cut filter. Both <em>G_CC</em> and <em>cameraNDVI</em> showed similar, but somewhat unique, patterns in canopy greenness and VIS vs. NIR reflectance, across various ecosystems, indicating their consistent ability to record phenological variability. However, we did find that at most sites, <em>G_CC</em> time series had less variability and fewer outliers, representing a smoother signal of canopy greenness and phenology. Overall, PhenoCam greenness as measured by both <em>G_CC</em> and <em>cameraNDVI</em> provides expanded opportunities for studying phenology and tracking ecological changes, with potential applications to the evaluation of satellite data products, earth system and ecosystem modeling, and understanding phenologically mediated ecosystem processes. The PhenoCam V3.0 data release is publicly available for download from the Oak Ridge National Lab Distributed Active Archive Center: the source imagery used to derive phenology information is available at <a href="https://doi.org/10.3334/ORNLDAAC/2364" target="_blank" rel="noopener">https://doi.org/10.3334/ORNLDAAC/2364</a> (Ballou et al., 2025), and the summarized phenology data are available at <a href="https://doi.org/10.3334/ORNLDAAC/2389" target="_blank" rel="noopener">https://doi.org/10.3334/ORNLDAAC/2389</a> (Zimmerman et al., 2025).

BODIPYs have a well-established role in biological sciences as chemosensors and versatile biological markers due to their chemical reactivity, which allows for fine-tuning of their photophysical characteristics. In this work, we combined the unique reactivity of arylazo sulfones with the advantages of a "sunflow" reactor to develop a fast, efficient, and versatile method for the photochemical arylation of BODIPYs and other chromophores. This approach resulted in red-shifted emitting fluorophores due to extended electronic delocalization at the 3- and 5-positions of the BODIPY core. This method represents an advantageous approach for BODIPY functionalization compared to existing strategies.

The current paper reviews examples of working with organizational leaders and integrating adaption‐innovation (A‐I) theory and its associated psychometric, the Kirton's Adaption‐Innovation Inventory (KAI; Kirton, 1985). Three specific in‐depth cases are reviewed and analyzed, and a series of learning insights are shared. A set of key enabling factors are argued to transform A‐I related insights to valuable actions. These include emotional intelligence capabilities focused on self‐and‐others; the role of a structured learning process to aid reflection and action; effective coping behavior that sustains the options for action; and examples of the sourcing and use of diversity. Areas for further research into practice are also described.

Xanthomonas wilt of enset is a major bacterial disease affecting the entire enset-growing belt in the southern Ethiopian highlands, impacting food security and livelihoods of small-holder subsistence farmers. Through extensive interviews and field validation visits with 354 households covering 19 communities (kebeles), we show 70% of the farms to present current and past enset Xanthomonas wilt infections, with a median of 18% cumulative plant loss across affected years since disease appearance. Training by extension services proved critical for building up farmers&rsquo; knowledge on disease management, the effective implementation of disease management, and farmers&rsquo; persistence to continue with enset production even when dealing with widespread infestations. After receiving training, farmers were ten times more likely to implement targeted hygienic practices and only half as likely to implement non-recommended cultural practices. Nevertheless, training in disease management did not guarantee consistent implementation by farmers. Farmers remained reluctant to dispose of infected plants, a critical part of Xanthomonas wilt management. Moreover, farmers mostly applied disease management practices once infected plants are observed on the farm, but were unaware of preventive approaches. Extension services had also not reached all communities, with 64% of the households not having received training at the time of the survey. Key words: Disease management, Ethiopian highlands, farmer training, household surveys, Xanthomonas campestris pv. musacearum

Ninety backyard gardens in the south-eastern Ethiopian highlands with enset (Ensete ventricosum (Welw.) Cheesman), banana and various annual crops were assessed for the presence of enset root mealybugs (Paraputo ensete (Williams &amp; Matile-Ferrero)). This study presents the first observation of enset root mealybugs on banana. This pest has until now been exclusively recorded on enset in Ethiopia. In the Dilla Zuria district of the Gedeo zone, southern Ethiopia, infested banana mats of the &lsquo;Pisang Awak&rsquo; (ABB genome) landrace were observed adjacent to infested enset plants in three small-holder backyard gardens. As roots of banana mats and enset plants were overlapping and intertwined, and large numbers of mealybugs were observed on enset roots, possibly representing an overpopulation, the observed mealybugs on banana might have represented a &ldquo;chance infestation&quot;. The smaller size of mealybugs on banana roots might indicate a non-optimal host status of this crop. Experimental choice and no-choice pot trials however provided another indication of the possible host potential of &lsquo;Pisang Awak&rsquo; and of an additional banana cultivar &lsquo;Matooke&rsquo; (AAA-East African Highland). The enset root mealybug was able to fully develop, produce viable offspring and survive on both banana cultivars. Not all investigated banana cultivars presented this host status, and the susceptibility of most Musa cultivars remains low. Key words: Choice trials, corm, crawlers, enset suckers, field survey, Musa cultivars, no-choice trials, roots, screenhouse pot trials.

The present study aimed to obtain an efficient liquid nitrogen fertilizer from the by-product of anaerobic digestion for its subsequent use in the production of cyanobacteria (Spirulina). A simple recovery technology was tested based on the stripping and acid absorption, modifying temperature (50 and 70 °C) and pH (10 and 12), of the ammonia nitrogen contained in the digestate produced in a large-scale plant treating livestock manure and grass silage. The results demonstrated how, at a relatively low temperature (50 °C), using sulfuric and citric acid solution, it is possible to recover nitrogen from a digestate in the form of ammonium sulfate and ammonium citrate with yields of 70% and 72.1% respectively. By carrying out Spirulina growth tests, promising results were obtained under semicontinuous production, with a maximum dry biomass daily productivity of 0.344 g L−1 day−1 with ammonium sulfate and 0.246 gDW L−1 day−1 with ammonium citrate. The results showed that nitrogen can be efficiently recovered on site by using the organic acid, digestate and waste heat from anaerobic digestion for Spirulina biomass production.