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Abstract Background Several surveys conducted at slaughter sites have highlighted that gastric lesions are a widespread issue in fattening pigs, mainly due to feeding regimes. Diets with small particle sizes and low fibre contents guarantee high digestibility and performance but generate more rapid stomach emptying with a negative effect on gastric mucosa integrity. Providing fattening pigs with fibrous materials (e.g., straw provided in racks) or coarse fibrous ingredients (e.g., coarse silages) reduced the presence of gastric ulcers. The present research compares a traditional corn-soy-based diet with an experimental diet where bran and a portion of corn meal was substituted with whole ear and whole plant corn silages at the maximum dosages permitted by new Protected Designation of Origin for Italian dry-cured ham (20 and 10% of DM, respectively). This study aimed to examine the impact of the inclusion of corn silages in the diet on the productive performance of heavy Italian pigs and their ability to mitigate gastric mucosa damage. Results The growth performances were satisfactory (750–800 g/d) given the advanced interval of growth of animals (from 120 to 180 kg). However, the inclusion of corn silages tended to reduce the growth rate by 5–6% due to the reduction of organic matter digestibility, without compromising the slaughter traits or the back-fat fatty acid profile. The experimental diet substantially affected both stomach development and mucosal integrity. The first consequence was an increase in stomach weight of approximately 6% (P

Residual feed intake (RFI), a widespread index used to measure animal feed efficiency, is influenced by various individual biological factors related to inter-animal variation that need to be assessed. Herein, 30 Simmental bulls, raised under the same farm conditions, were divided on the basis of RFI values into a high efficient group (HE, RFI = − 1.18 ± 0.33 kg DM/d, n = 15) and a low efficient group (LE, RFI = 0.92 ± 0.35 kg DM/d, n = 15). Subsequently, bulls were slaughtered at an average BW of 734 ± 39.4 kg. Their ruminal fermentation traits were analysed immediately after slaughtering and after 24 h of in vitro incubation. Furthermore, ruminal micro-biota composition and ruminal papillae morphology were examined. The LE group exhibited a higher propionate concentration as a percentage of total volatile fatty acids (17.3 vs 16.1%, P = 0.04) in the rumen fluid collected during slaughtering, which was also confirmed after in vitro fermentation (16.6 vs 15.4% respectively for LE and HE, P = 0.01). This phenomenon resulted in a significant alteration in the acetate−to−propionate ratio (A:P) with higher values for the HE group, both after slaughter (4.01 vs 3.66, P = 0.02) and after in vitro incubation (3.78 vs 3.66, P = 0.02). Methane production was similar in both groups either as absolute production (227 vs 218 mL for HE and LE, respectively) or expressed as a percentage of total gas (approximately 22%). Even if significant differences (P

Mediterranean environments are characterized by high spatial and temporal heterogeneity due to their climatological, lithological, soil and vegetation geo-diversity and their high population density which cause growing land-use transformations at the rural-urban fringe. Remote sensing mapping and monitoring land cover in these environments under such conditions is a challenging task. Instead of the common per pixel approach we suggest combining application of an object-oriented classification based on image objects separation through edge detection with unsupervised classification. The main elements of our methodology are: (1) separating image areas into vegetation/ non-vegetation regions utilizing NDVI threshold; (2) calculation of the spatial variance at different bands; (3) image objects extraction through enhancement of the differences between edge pixels and regions of homogeneity; (4) per-object classification for the homogenous areas; (5) overlaying large unclassified image areas by the results of ISODATA (Iterative Self-Organizing Data Analysis) unsupervised classification. Our methodology was applied on multi-spectral images acquired by the VENμS remote sensing system. The study area consists of a typical rural area in semi-arid climate regions undergoing increasing urbanization. Six test areas were selected representing different spatial combinations of natural/ planted forests, agriculture and built-up land-use/ land cover types. While bare fields were poorly classified, areas of low vegetation cover were classified with producer/user accuracies below 60%, built-up areas and roads, cultivated areas, shrublands and bata (dwarf-shrubs) and rocky areas gained good producer/ user classification accuracies.