New approaches to selecting a scan-sampling method for chicken behavioral observations and their practical implications.

The use of the scan-sampling method, especially when a large amount of data is collected, has become widespread in behavioral studies. However, there are no specific guidelines regarding the choice of the sampling interval in different conditions. Thus, establishing a standard approach for video analysis represents an important step forward within the scientific community. In the present work, we hypothesized that the length of the sampling interval could influence the results of chicken behavioral study, for which we evaluated the reliability, accuracy, and validity of three different sampling intervals (10, 15 and 30 min). The Bland-Altman test was proposed as an innovative approach to compare sampling intervals and support researcher choices. Moreover, these sampling intervals were applied to compare the behavior of 4 chicken genotypes kept under free-range conditions. The Bland-Altman plots suggested that sampling intervals greater than 10 min lead to biases in the estimation of rare behaviors, such as "Attacking". In contrast, the 30-min sampling interval was able to detect differences among genotypes in high-occurrence behaviors, such as those associated with locomotory activity. Thus, from a practical viewpoint, when a broad characterization of chicken genotypes is required, the 30-min scan-sampling interval might be suggested as a good compromise between resources and results.

Intake of nutrients (polyunsaturated fatty acids, tocols, and carotenes) and storage efficiency in different slow-growing chickens genotypes reared in extensive systems.

An extensive rearing system (ERS) for poultry requires an outdoor run, which enhances the foraging activity of chickens. Slow-growing (SG) strains are more adapted to ERS than fast-growing (FG); and generally, have higher levels of bioactive compounds in their meat. The aim of this paper was to assess the storage efficiency of n-3 and n-6 polyunsaturated fatty acids (PUFA), tocols and carotenes in the meat of seven commercial SG genotypes (SG1-7). One hundred SG chicks/strain of both sexes were included and their walking activity (High- or Low-W) was classified: SG1-4, HW comprised more than 10% of the time budget, and SG5-7, less than 10% (LW). Chickens were reared in pens (4 pens/strain) with indoor (0.10 m2/bird) and outdoor (4 m2/bird) areas, and they were fed the same diet ad libitum (starter feed for 1-21 d, grower feed from 22 d to slaughter at 81 d). The chickens were weighed weekly; feed consumption and grass intake were also estimated. At 81 days of age, 32 chickens/genotype were selected on the basis of the average weight (1:1, M:F) and slaughtered. The breast, thigh and drumstick meat were excised from 30 carcasses/genotype, sampled and stored at -20°C until analysis. Nutrients (e.g., n-3, n-6, carotenes and tocols) of feed, grass and meat were analyzed. The storage efficiency of nutrients was estimated as the ratio between the amount deposited in the body muscles (OUT) and the dietary intake (feed and grass, IN). The genotype affected chickens foraging behavior and the intake of nutrients. For SG1, SG2 and SG3, more than 50% of the intake of n-3 came from grass, whereas in the other genotypes, less than 20%. Accordingly, chickens that foraged more showed better meat nutritional profiles (less fat, more n-3 and antioxidants), which, in ERS, was ascribed to grass ingestion. However, the storage efficiency of nutrients into meat was inversely correlated with the grass intake: strains with higher grass intake (SG1, SG2, and SG3) had lower storage rates. Several hypotheses were proposed to explain these trends.

Measuring Comfort Behaviours in Laying Hens Using Deep-Learning Tools.

Image analysis using machine learning (ML) algorithms could provide a measure of animal welfare by measuring comfort behaviours and undesired behaviours. Using a PLF technique based on images, the present study aimed to test a machine learning tool for measuring the number of hens on the ground and identifying the number of dust-bathing hens in an experimental aviary. In addition, two YOLO (You Only Look Once) models were compared. YOLOv4-tiny needed about 4.26 h to train for 6000 epochs, compared to about 23.2 h for the full models of YOLOv4. In validation, the performance of the two models in terms of precision, recall, harmonic mean of precision and recall, and mean average precision (mAP) did not differ, while the value of frame per second was lower in YOLOv4 compared to the tiny version (31.35 vs. 208.5). The mAP stands at about 94% for the classification of hens on the floor, while the classification of dust-bathing hens was poor (28.2% in the YOLOv4-tiny compared to 31.6% in YOLOv4). In conclusion, ML successfully identified laying hens on the floor, whereas other PLF tools must be tested for the classification of dust-bathing hens.

The Assessment of a Multifactorial Score for the Adaptability Evaluation of Six Poultry Genotypes to the Organic System.

This study aimed to develop an adaptability score (AS) for chicken strains, which includes behavioral, plumage conditions, and body lesion indicators through a multifactorial approach. A total of 600 male chickens from 6 poultry genotypes-Ranger Classic (R1), Ranger Gold (R2), Rowan Ranger (R3), Hubbard Red JA (A), CY Gen 5 × JA87 (CY), and M22 × JA87 (M)-were reared under organic conditions, fed ad libitum, and individually weighed weekly to calculate the daily weight gain (DWG). The behavioral observations consisted of the explorative attitude (EA), recorded at 21 days, and the behavioral patterns (BPs) recorded the week before the slaughter. The AS was established by a principal component analysis, and the AS of these genotypes was compared. Moreover, the effect of DWG and genotype on the AS was evaluated by univariable and multivariable regression models. Although the DWG and genotype were strictly dependent, genotype was the most important factor affecting the AS. In fact, its effect was significant both in univariable (p < 0.001) and multivariable models (p < 0.001). Conversely, the DWG was significant only in the univariable and lost significance when the effect of genotype was introduced in the model.

How the kinetic behavior of organic chickens affects productive performance and blood and meat oxidative status: a study of six poultry genotypes.

The aim of this study was to analyze the relationship between the kinetic behavior, carcass characteristics, oxidative status (blood and meat), and meat fatty acid profiles of 6 organically reared slower growing chicken genotypes (SrG). One hundred male chickens of 6 SrG were used: Ranger Classic (RC), Ranger Gold (RG), Rowan Ranger (RR), RedJA (RJ), CY Gen 5 JA87 (CY), and M22 × JA87 (M). Twenty chickens/genotype were selected to analyze behavior, while, 15 individuals were slaughtered and different traits were analyzed in the blood and drumstick meat. The variables were grouped into different principal components: kinetic activity (PC1, with explorative attitude as the highest score), productive performance (PC2, carcass and head/feet yields), blood (PC3, carbonyls, and TBARS) and meat (PC6, thiols, and TBARS) markers, technological traits (PC4, pH, and color), proximate meat composition (PCA5, moisture, lipids, protein, and ash), fatty acid profile, and nutritional indexes (PC7, IP, and PUFAn-3). Uni- and bivariate analyses showed a strong positive association between kinetic behaviors and blood and meat oxidation and a medium positive association with fatty acid profile and nutritional indexes, whereas a negative association was found between productive performance loads and the technological traits of meat. Generalized linear models showed that all PCs were influenced by genotype. In particular, CY and M resulted as less active genotypes; conversely, RR showed more kinetic activity, whereas RJ, RG, and RC exhibited intermediate levels of activity. Cluster analysis of kinetic behavior and blood or meat oxidative status highlighted 2 groups: nonwalking (NW: CY and M) and walking (W: RC, RG, RR, and RJ) animals. However, in the W group, another was visualized, constituted by genotypes with high kinetic activity resulting in the worst oxidative balance (Walking not trained-genotypes, Wnt: RR and RJ). The present results confirmed that the kinetic behavior of SrG genotypes is negatively correlated with productive performance. Furthermore, a significant association between kinetic behavior and blood (positively correlated) or meat (negatively correlated) oxidative status was noted. Such differences are mainly due to the intrinsic response of the genotypes used (i.e., training-walking capacity).