The effects of chestnut orchard microclimate on burr development

Chestnut crop is regaining its fame worldwide with powerful investment perspectives. Unluckily the climate change effects are posing high threat to its cultivation with less available resources and increased production cost both in traditional and specialized orchards. Additionally, the chestnut physiological knowledge is still limited, especially as concern the burr development (i.e., the economical production target) and its relationship with the environmental parameters. The aim of the present study was to evaluate the seasonal, daily, and hourly burr growth pattern associated to environmental parameters for improving physiological knowledge on this species. The study was carried out in a traditional rainfed sweet chestnut orchard located in the Tuscan-Emilian Apennines (Monterenzio, Italy). The chestnut burr growth was measured, along the entire season, both with a digital calliper and through the use of plant-based sensors (fruit-gauges) that permitted to measure, in real-time, the burr growth pattern. Environmental data were recorded by a weather station placed in the middle of the orchard. Results evidenced a higher burr growth rate, in the last part of the season (from middle-end of August to full fall) while the daily growing pattern was characterized by increased oscillation, along the season, of night-swelling and daily-shrinkage. The night-swelling was found to be influenced by high nocturnal air relative humidity while the daily-shrinkage was influenced by the higher wind speed, solar radiation and vapour pressure deficit. Thus, the burr daily net growth can be associated, depending on the phenological stages, to environmental parameters. Precipitation but especially the atmosphere humidity, in September and October, were the main external drivers of burr daily net growth. These results could be promising for the adoption of sustainable (e.g., late season grass mowing, sprinkler irrigation) and smart practices for improving chestnut management in both traditional and specialized orchards

Irrigation improves tree physiological performances and nut quality in sweet chestnut

Italy is one of the most important world chestnut producers. The majority of traditional sweet chestnut orchards are still non-irrigated since they are typically located in mountain-hill areas usually characterized by environmental conditions that are not limiting for the vegetative and reproductive growth of this fruit tree crop. Nowadays, the increase of summer temperatures and the decrease of rainfall are affecting negatively chestnut physiological performances and productivity. The adoption of scheduled irrigation practices, in light also of the limited water availability/possibility of storage (e.g., artificial lakes, reservoirs) of these areas, should become part of chestnut orchard management. The aim of the present study was to evaluate the effect of irrigation on sweet chestnut physiology, nut quality and yield. The study was carried out in 2020 in a traditional chestnut orchard of the “Marron Buono di Marradi” ecotype, located in the Tuscan-Emilian Apennines (Marradi, Italy). The experimental design compared trees irrigated between August and September with a non-irrigated control. Leaf gas exchange and plant water status were monitored during the growing season and, nut quality and yield were assessed at harvest. Results showed that irrigated trees exhibited, in middle September, higher photosynthesis, transpiration, stomatal conductance and stem water potentials compared to the non-irrigated control trees. Nut size was significantly smaller in non-irrigated trees than in irrigated ones while the yield was not statistically affected by the irrigation treatment. Despite the favourable mild and rainy weather conditions occurred in 2020, the application of irrigation during the nut filling phase (e.g., late summer) was beneficial for enhancing sweet chestnut physiological performances and for improving nut quality

Single-shot convolution neural networks for real-time fruit detection within the tree

Image/video processing for fruit detection in the tree using hard-coded feature extraction algorithms has shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This architecture takes the input image and divides into AxA grid, where A is a configurable hyper-parameter that defines the fineness of the grid. To each grid cell an image detection and localization algorithm is applied. Each of those cells is responsible to predict bounding boxes and confidence score for fruit (apple and pear in the case of this study) detected in that cell. We want this confidence score to be high if a fruit exists in a cell, otherwise to be zero, if no fruit is in the cell. More than 100 images of apple and pear trees were taken. Each tree image with approximately 50 fruits, that at the end resulted on more than 5000 images of apple and pear fruits each. Labeling images for training consisted on manually specifying the bounding boxes for fruits, where (x, y) are the center coordinates of the box and (w, h) are width and height. This architecture showed an accuracy of more than 90% fruit detection. Based on correlation between number of visible fruits, detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Processing speed is higher than 20 FPS which is fast enough for any grasping/harvesting robotic arm or other real-time applications. HIGHLIGHTS: Using new convolutional deep learning techniques based on single-shot detectors to detect and count fruits (apple and pear) within the tree canopy

The fate of bacteria in urban wastewater-irrigated peach tree: a seasonal evaluation from soil to canopy

Irrigation with wastewater can be a solution to preserve and mitigate freshwater demand, in particular during drought periods. Unfortunately, wastewater, although being treated at different levels, could be a carrier of human pathogens (e.g., E. coli) and potentially contaminate crops for human consumptions.This study investigated the seasonal microbiological concentrations, on soil, shoot and fruit tissues of potted peach trees, following two irrigation treatments: freshwater (FW) and secondary urban wastewater without the final disinfection treatment (SW). E. coli was only detected in SW irrigated soil, whereas total coliforms (TC) and total bacteria counts (TBC) were similar in both treatments throughout the season. EndophyticE. coli, Salmonella spp. and TC were not detected in shoot and fruit, but a higher presence of total bacteria (TBC) was observed in SW-irrigated tree compared to FWirrigated tree. In particular, SW shoots had a higher load compared to fruits, thus showing a potential effect of leaf transpiration, that promoted the transfer of water-borne bacteria from soil to the epigeal part (shoot). The adoption of low-quality SW (even above the microbiological limits of the European Regulation 2020/741 for wastewater re-use in agriculture), when a drip irrigation method is applied, could be a valid alternative to save fresh water without compromising fruit safety

Hidden clusters: the articulation of agglomeration in City Regions

For many years, local economic development has been driven by the desire to maintain, attract and nurture clusters of economic activity in targeted industrial sectors. However, where clusters are not conventionally sector-based, public policy needs to develop alternative approaches to leverage the economic benefits and realise competitive advantage. Drawing on a study of the Sheffield City Region (SCR), the paper explores the challenge of leveraging ‘hidden’ cross-sectoral clusters, which do not fit dominant discourses of agglomeration-led growth. We posit that it is the cross-sectoral connections and networks in the SCR which represent its key strength, yet these are only partially reflected by current place marketing and policy considerations, and, in many ways, are overlooked and thus remain ‘hidden’. The paper argues that the competitive advantage of the SCR is undermined when it characterises clusters in terms of industrial sectors, and instead needs to articulate its strengths as a strategically important industrial centre. The paper concludes by drawing out a number of implications for academic theory and policy development

Contratto a tutele crescenti e naspi

Il volume suddiviso in tre sezioni affronta, nelle prime due, gli aspetti salienti del d.lgs. n. 23/2015 con riferimento al significato da attribuire al contratto a tutele crescenti e al nuovo regime di tutela dei licenziamenti individuali e collettivi. La terza sezione è dedicata all'analisi della nuova assicurazione sociale per l'impiego ed ai sistemi di politica attiva per la ricerca di nuova occupazion

Thickness dependence of the amplified spontaneous emission threshold and operational stability in poly(9,9-dioctylfluorene) active waveguides

We investigate the thickness dependence of the amplified spontaneous emission (ASE) threshold and operational lifetime in air-poly(9,9-dioctylfluorene)(PF8)-glass asymmetric active waveguides. We show that the ASE threshold decreases with the film thickness up to about 200 nm, and increases for higher thicknesses. The ASE operational lifetime increases with the thickness up to about 300 nm, and it is almost thickness independent for higher thickness. We show that the observed results are related to the guided mode confinement in the waveguide and to the spatial overlap between the guided modes and the excited region in the film