Abstract This study examined associations between αS1- and κ-casein genotypes and baseline and dynamic regression components of semen quality in Murciano-Granadina bucks using cubic regression models applied to 6,868 ejaculates collected over 10 years, with age at sperm collection as the time axis. Regression decomposition into baseline (b0), linear (b1), curvature (b2), and cubic tren (b3) coefficients enabled quantification of static trait levels and age-dependent dynamics, revealing substantial coefficient variability. For αS1-casein, genotype was associated with baseline progressive motility and total added semen volume (p < 0.05), with moderate to large discriminant effects for progressive motility (ηp2 = 0.277, 95% CI 0.03–0.48) and curvature-related motility and Acrosome Integrity/Intact acrosomes components (ηp2 = 0.212–0.245), indicating genotype-related differences in modeled semen quality rather than direct fertility effects. In contrast, κ-casein genotypes showed predominantly small to moderate effects, mainly related to ejaculate volume dynamics and baseline sperm concentration (ηp2 = 0.131–0.138), while most other components were negligible. Cubic terms captured subtle non-linear trajectories, particularly for sperm concentration and endosmosis, highlighting their relevance for long-term trait stability. Discriminant analyses indicated model-based separation of αS1–κ genotype combinations, with EE–AA (n = 1) and BE–AA (n = 3) aligning with higher sperm motility, concentration, and favorable membrane-integrity responses, whereas BB–BB (n = 3) aligned with higher semen volume and lower quality traits. Validation procedures were method-specific. CDA using leave-one-out cross-validation showed stronger discrimination for αS1-casein (74.03%) than κ-casein (64.94%), driven by perfect classification of the dominant BE genotype and substantial misclassification of rarer genotypes, whereas CHAID using 10-fold cross-validation showed moderate predictive performance with lower risk for αS1-casein (0.325, SE = 0.074; accuracy = 67.53%) than κ-casein (0.429, SE = 0.079; accuracy = 57.14%). Press’ Q statistics confirmed that classification accuracy for both genotypes exceeded chance expectation across methods (CDA: αS1-casein Q = 182.40, κ-casein Q = 34.60; CHAID: αS1-casein Q = 143.44, κ-casein Q = 19.64; p < 0.05), with consistently stronger discrimination for αS1-casein genotypes. Overall, baseline and dynamic regression components capture biologically relevant, exploratory associations between casein polymorphisms and semen quality dynamics, supporting their integration into genomics-informed reproductive management strategies.

The Iberian Peninsula hosts a rich diversity of sheep genetic resources shaped by contrasting environments, historical management practices, and varying levels of endangerment. This study provides a comprehensive demographic characterisation of 16 Iberian sheep breeds (12 autochthonous and 4 integrated) using complete pedigree records supplied by breeders’ associations. Population structure was evaluated through census composition, population fragmentation, trends in sire and dam registrations, reproductive patterns, gender ratios, pedigree completeness, and generation intervals. Marked differences were observed among breeds. Integrated breeds consistently exhibited the highest pedigree completeness and a more intensive reproductive structure, characterised by fewer breeding animals but a higher number of offspring per parent. In contrast, many endangered Spanish breeds showed incomplete pedigree records, fewer registered sires and dams, unbalanced sex ratios and prolonged use of older breeding animals, indicating greater demographic vulnerability. Portuguese local breeds displayed more complete pedigree records, together with relatively high numbers of offspring per parent and/or extended reproductive use of breeding animals. Most breeds showed generation intervals close to four years, although longer intervals were observed in the most endangered populations. These findings highlight substantial differences in demographic structure and pedigree quality among Iberian sheep breeds, providing a demographic baseline to improve pedigree recording and support future conservation and breeding strategies, while reinforcing the role of breeders’ associations in preserving the genetic diversity.

This study examined how climatic variability affects reproductive performance in Murciano-Granadina does, analysing 21,757 animals and 32,693 artificial insemination (AI) records collected from 2010 to 2019. Comprehensive weather data (temperature, rainfall, wind speed and gusts, wind direction, barometric pressure, photoperiod and altitude) were integrated with fertility outcomes to model environment-reproduction interactions. Fertility was assessed using three indexes: daily fertility rate, fertility per buck batch per day, and fertility per semen type (fresh vs. frozen). Fresh semen achieved higher mean fertility (56.17%) than frozen semen (37.75%) with lower variability (SD 16.52 vs. 15.96), confirming its superior integrity. Regularised canonical correlation analysis (rCCA) revealed strong multivariate links between climatic factors and fertility. For frozen semen, two canonical functions explained 66.22% of shared variance (F1 = 0.230; F2 = 0.164), indicating sensitivity to wind instability and low temperatures. Fresh semen showed a more stable pattern, with the first function alone explaining 96.79% of variance (F1 = 0.277); fertility was optimal at minimum temperatures >11.35 degrees C, maximum <25.7 degrees C, altitudes <489 m and rainfall <2.5 mm. Wind gusts above 4.5 m/s negatively affected conception, especially for cryopreserved semen. Cross-validation confirmed model robustness, with higher predictive accuracy for frozen semen (CV = 0.642) than for fresh semen (0.247), reflecting greater environmental sensitivity of cryopreserved gametes. These findings show that climatic data can inform AI scheduling, enabling semen-type-specific management to improve fertility, accelerate genetic gain and enhance the sustainability of breeding programs under variable Mediterranean conditions. HIGHLIGHTS Fresh semen fertility 18.4% higher than frozen semen across 32,693 Als. Thermal comfort zone' Tmm 11 degrees C and Tmax <25.7 degrees C maximises fertility. ICCA captured 97% variance in fresh semen vs. 66% in frozen semen. Wind gusts (4.5 m/s) and rainfall reduced conception, especially with frozen semen. Predictive models enable climate-aware, semen-type-specific Al scheduling.

The aim of this study was to determine whether smart data informed genetic variability for dairy merit traits may act as a predictor for fertility dynamics in Murciano-Granadina does. A total of 17,012 AI records performed on 6706 does were used to model fertility across insemination day, buck batch × day, and semen type, defining three fertility dynamics indicators. Cubic regression models resulted best-fitting alternatives to characterize fertility indicators and estimate baseline levels, temporal trends, and nonlinear patterns. Regularized canonical correlation analysis (rCCA) was then applied to relate a first variable set comprising fertility indicators cubic regression parameters to a second set comprising predicted breeding values (PBVs) for dairy merit traits -linear appraisal system (LAS) zoometrics and milk yield and composition traits-. First two canonical functions explained >90% shared variability, linking fertility dynamics to body structure (udder width, rump conformation, chest depth). Milk composition PBVs (dry matter, lactose) were synergistic with fertility, while milk yield and body size were antagonistic. Somatic cell count PBVs negatively correlated with fertility, highlighting the role of udder integrity. Fertility curve parameters also provided information beyond milk and conformation PBVs, supporting their value as complementary genetic descriptors. Overall, fertility dynamics follow nonlinear trajectories captured by cubic regression and show structured relationships with dairy-merit PBVs, indicating that genetic variability in conformation and milk composition predicts fertility over time. Accordingly, integrating fertility curve parameters with dairy-merit PBVs can enable smart data-driven genetic evaluations and improve selection for structural soundness, milk quality, and reproductive performance in Murciano-Granadina goats.

This study examined the influence of environmental variables on semen quality in Murciano-Granadina bucks over a 10-year period (2010-2019), analyzing 115 males and 6868 ejaculates. Regularized Canonical Correlation Analysis (rCCA) was applied to overcome multicollinearity and improve prediction of relationships between climatic factors and semen traits. Results showed that bucks displayed resilience to high temperatures, with positive associations between temperature and sperm motility, viability, and morphology. In contrast, cold stress, particularly when combined with strong wind gusts and low barometric pressure, negatively impacted ejaculate volume, motility, and membrane integrity. Rainfall also influenced sperm concentration and acrosome integrity. The first two canonical functions explained over 84% of shared variance, highlighting thermo-biological and atmospheric gradients as key determinants of reproductive performance. These findings demonstrate the utility of rCCA as a predictive tool and underscore the importance of integrating climatic monitoring into artificial insemination programs to enhance semen management, breeding efficiency, and the sustainability of goat production.

This study aimed to use a smart, data-driven analytical framework based on algorithmic rule-learning methods to explain and optimize artificial insemination protocols in Murciano-Granadina does. A total of 10,818 artificial inseminations from 8,121 does (45.16 +/- 20.66 months old), using semen from 55 bucks, were analysed using canonical discriminant analysis and CHAID decision trees. The CHAID analysis followed a hierarchical structure, with variables selected sequentially according to explanatory power. Birth type acted as the primary predictor, followed by parity and age at insemination as secondary predictors, while equine chorionic gonadotropin (eCG) dose, season, and semen type contributed at lower hierarchical levels. Overall kid survival was high, with 98.4 % live births and 1.6 % stillbirths, mainly in high-order litters. Birth type emerged as the dominant determinant of prolificacy and kid survival, followed by parity, age at insemination, and eCG dose. Seasonal effects and semen type refined outcomes, with spring inseminations and fresh semen associated with greater homogeneity. Protocols combining these conditions with moderate eCG doses (250-300 IU) maximized prolificacy while minimizing stillbirth risk. Among successful inseminations, twin births predominated (57.2 %), followed by single (24.9 %) and triple (16.1 %), whereas large litters (>= 4 kids) were rare (1.9 %) and associated with increased neonatal mortality. A significant age & times; eCG interaction was observed, with reduced fertility and increased neonatal losses in older or repeatedly treated does. CHAID models showed high classification accuracy, with Press's Q values exceeding chance expectations, supporting precision, age-and parity-specific insemination protocols that enhance reproductive efficiency while reducing unnecessary hormonal use.

This study analyses relationships between milk yield, milk composition, and fertility in Murciano-Granadina dairy goats to test whether these associations reflect biological pathways underlying a production-fertility trade-off. Linear and non-linear patterns were examined using canonical discriminant analysis (CDA), regularized canonical correlation analysis (rCCA), and CHAID decision trees. The dataset included 32,693 artificial inseminations from 21,757 does and 29,390 milk records from 21,541 does; fertility was assessed using three indicators accounting for insemination timing and semen type. Mean fertility differed by semen preservation method, with fresh/chilled semen showing 18-20 percentage points higher fertility than frozen/thawed semen. Milk yield showed wide variability (8.7-6704.6 kg; mean 686.4 kg), whereas milk composition was relatively stable. After multicollinearity control (VIF > 178 to <5), CDA and rCCA revealed a low-dimensional structure dominated by a first canonical function, explaining 80.8-88.7% of discriminant variance and 97.3% of shared covariance. This dominant axis reflected an energy balance and metabolic partitioning pathway, with higher milk yield and solids concentration associated with reduced fertility; very high production (>2021 kg) consistently coincided with lower fertility, supporting a production-reproduction trade-off. A secondary axis represented an endocrine and lactation-timing pathway linking fertility to temporal variation in milk composition, while semen type constituted an autonomous semen-related pathway with the strongest standardized canonical coefficients. CHAID analysis identified non-linear relationships and biologically meaningful thresholds in milk traits, achieving ~44% classification accuracy and up to 87% reliability for high fertility. Optimal fertility was associated with intermediate ranges of milk yield and composition (protein 3.64-6.98%, fat 6.0-7.9%, dry matter 14-25%, lactose 5.6-6.5%, SCC < 5200 × 10³ cells/mL). Overall, milk yield and composition showed statistically robust but moderate associations with fertility, consistent with reproductive biology multifactorial nature of and gaining relevance when interpreted jointly with metabolic, endocrine, and semen-related factors.

Organic and free-range farming are becoming increasingly common practices in the turkey meat industry, although limited research has been conducted in uncontrolled environments. Therefore, the present work aims to define the growth pattern of an indigenous landrace and a commercial strain grown outdoors in southern Spain. To achieve this aim, the weights of Andalusian turkeys (1,826 and 1,797 records for males and females, respectively) and commercial toms (584 records) were recorded to test 19 linear and nonlinear mathematical functions. The suitability of the models was defined using the coefficient of determination (pseudo-R2), mean squared error, the Bayesian information criterion, the Akaike information criterion, and the corrected Akaike information criterion. Overall, 17, 16, and 13 models out of 19 fitted the growth curves of Andalusian females, Andalusian males, and commercial males, respectively. Most of the models showed Pseudo-R2 values greater than 0.90. According to the sigmoid models, the asymptotic weights were estimated to be close to 11.5, 5.5, and 23.0 kg for Andalusian males, females, and commercial males, respectively. The Andalusian females showed the earliest precocity at inflection and optimal slaughter age (3 and 5 mo, respectively), while the Andalusian males showed the latest precocity (4 and 7 mo, respectively). As expected, based on literature on other fast-growing strains, the commercial flock exhibited the greatest weight (8.96-11.44 kg) and growth rates (143.30-210.38 g/d). However, delayed ages at inflexion and optimal slaughter (3.7 and 5.8 mo, respectively) and lower maturity indexes were obtained compared to those in the bibliography. On the other hand, the age at inflection and maturity indices of Andalusian birds were similar to those of the commercial flock, in line with reports on other local breeds. Therefore, the environment had a significant impact on commercial birds reared outdoors, reducing their growth potential and efficiency. On the other hand, the growth of Andalusian birds seemed unaffected.

Artificial insemination (AI) success in Murciano-Granadina goats is influenced by a complex interaction of male, female, management, and environmental factors. This study aimed to identify the main drivers and threshold conditions affecting fertility outcomes in commercial AI programs using long-term field data. A dataset of 3,122 inseminations performed between 2010 and 2019 was analyzed using canonical discriminant analysis and CHAID decision trees. Multicollinearity screening excluded 24 redundant predictors out of an initial set of 52 variables, retaining 28 variables with the greatest explanatory power. The canonical model showed that the first six functions explained 72.4

The present study aims to characterize the quality attributes of Andalusian turkey meat and carcass, and to compare the results with those reported in others turkey populations worldwide. To this end, the quality attributes of 12 nine-month-old Andalusian toms reared in traditional husbandry conditions were studied. These traits included the weight and yield of the carcass and primal cuts, pH, water-retaining properties, and the proximate composition of the breast and thigh meat. These observations were compiled into a meta-analysis database comprising 157 reports of seven turkey genotypes collected from 63 studies worldwide. A principal component analysis was conducted to contextualize the meat and carcass quality traits of the Andalusian turkey within the registers of other turkey populations. The results showed that the weight of the carcass and primal cuts had a higher variability than the dressing yield (%) and physicochemical traits of meat. The Andalusian turkeys exhibited a semi-heavy carcass (± 7.60 kg) and components, with a low dressing percentage of the edible cut-offs such as breast (± 22.40%). However, the quality of the breast meat was outstanding in terms of proximate composition, and cooking and cooling losses, when compared to those reported in other turkey genotypes. Despite their greater production efficiency, the commercial alternative, which comprised commercial fast-growing hens or light hybrids, exhibited poorer meat quality. Therefore, the present study is one of the first meat and carcass characterization studies in a European turkey landrace. The results would help breeders to add value to their products, encouraging interested consumers to pay the extra cost of breeding.

This study evaluated the association between αS1- and κ-casein genotypes and semen quality traits in Murciano-Granadina bucks. The aim was to assess their potential relevance in dual-purpose breeding programs integrating milk production and reproductive performance. A total of 6,868 ejaculates from 115 bucks collected between 2010 and 2019 were analyzed under standardized collection and evaluation protocols. Semen traits exhibited substantial variability, particularly in ejaculate volume (mean 1.34 mL; range 0-5.0), sperm concentration (mean 3,619 × 106/mL), and motility parameters. Multivariate analyses revealed significant genotype-associated differentiation patterns for both casein systems. For αS1-casein, 2 canonical functions explained over 90% of the total multivariate variation, with sperm concentration, progressive motility, and membrane functionality (HOST) contributing most strongly to genotype discrimination (P < 0.0001). Bucks carrying AA and AB genotypes were associated with higher sperm output and ejaculate volume, whereas BB genotypes showed comparatively higher values for motility-related traits and acrosome integrity. For κ-casein, a single canonical function accounted for most of the multivariate discrimination (89.0%), primarily driven by progressive and total motility (P < 0.0001). Similarly, AA and AB genotypes were linked to higher sperm production, while BB genotypes exhibited relatively enhanced motility stability and acrosomal integrity. Complementary CHAID classification analyses supported these patterns and achieved moderate classification accuracy (∼66%). However, balanced accuracy remained low (17.9-35.2%), reflecting genotype imbalance. Standardized effect sizes derived from mixed models were small (partial R2 range: 0.002-0.037), consistent with the polygenic nature of reproductive traits. Overall, the results demonstrate statistically significant multivariate associations between casein genotypes and semen quality traits. While these findings do not imply causal relationships, they suggest that casein variants may provide complementary information for multi-trait selection schemes. However, their individual effects are modest, and further validation in independent populations and functional studies is required before practical implementation in breeding programs.

Indigenous dog breeds of the Balearic Islands constitute an essential part of regional biodiversity and cultural heritage, yet many face endangerment due to shrinking population sizes and delayed institutional recognition. This study assessed the usefulness of multivariate statistics and data-mining tools to analyse temporal trends and census evolution in these breeds, with emphasis on the influence of breeder associations and conservation structures. Canonical discriminant analysis effectively distinguished demographic patterns among breeds while identifying and accounting for multicollinearity, enabling the exclusion of unreliable variables. A single discriminant function captured nearly all variability, highlighting its strong ability to separate breeds according to registry and census characteristics, with registry size emerging as the dominant discriminatory factor. Decision-tree modelling showed that rapid creation and official recognition of breeder associations accelerated conservation progress by promoting organisational stability and earlier institutional protection. Early foundational actions-such as systematic record keeping and development of breed standards-substantially reduced the time required for official recognition. Functional classification also shaped vulnerability: herding and guarding breeds exhibited greater susceptibility to census decline. Additionally, the number of breeding females recorded in foundational censuses emerged as a critical long-term sustainability indicator. The integrated application of multivariate and data-mining approaches provides a robust analytical framework for understanding demographic dynamics in endangered dog populations. Results emphasise that rapid organisational development, early pedigree and phenotypic documentation, and preservation of traditional functional roles, particularly hunting, are central to the effective conservation of Balearic indigenous dog breeds. These insights offer actionable guidance for breeders, associations, and policymakers aiming to safeguard genetic diversity and cultural heritage.

This study examines the relationship between linear appraisal and long-term fertility dynamics in Murciano-Granadina goats, analyzing a dataset of 21,757 does and 32,693 artificial insemination (AI) records collected over 10 years. Fertility was assessed by day of insemination, buck batch, and semen type (fresh/chilled vs. frozen/thawed). Descriptive fertility rates revealed mean values of 52.6 % (day of insemination), 53.4 % (buck batch/day), and 48.3 % (semen type). Canonical correlation analysis (rCCA) showed significant but moderate associations between fertility and morphology (canonical correlations: 0.191, 0.144, and 0.045). First two canonical functions explained 96.7 % of variability, though redundancy coefficients were low (≤0.0146), indicating that morphology accounted for only a moderate proportion of fertility variability. Specific LAS traits provided subtle insights: chest width and rump width negatively correlated with fertility (r = -0.14 and -0.11, respectively), while bone quality showed the highest positive association (r ≈ 0.05). Rear udder insertion height and hind leg side view were positively linked to fertility resilience under frozen/thawed semen use. Conversely, excessively deep udders and large body size tended to compromise fertility, likely through mastitis risk or negative energy balance. Findings suggest skeletal robustness, udder attachment, and hind leg conformation support both fertility and productive longevity, while extreme dairy specialization may reduce reproductive outcomes. Overall, fertility in Murciano-Granadina does is multifactorial and moderately explained by linear appraisal traits, hence integrating morphological evaluation with artificial insemination strategies may enhance selection decisions. Future breeding programs should prioritize bone quality, functional udder morphology, and locomotor soundness to balance milk productivity with reproductive efficiency.

Background: Locally adapted goat populations represent important reservoirs of genetic diversity and play a crucial role in the sustainability of livestock production systems, particularly in marginal environments. However, many of these populations are currently threatened by genetic erosion caused by crossbreeding with highly specialized commercial breeds. Although previous studies have described the genetic diversity of several goat populations from South America and the Iberian Peninsula, the influence of geographic factors on the genetic structure of these populations remains insufficiently understood. In this study, we investigated the influence of geographic distance and spatial factors on the genetic diversity, population structure, and relationships among locally adapted goat populations from Brazil, Spain, and Ecuador. Methods: A total of 561 goats representing 15 populations were genotyped using a panel of 23 microsatellite markers. The dataset included six locally adapted Brazilian breeds, three Spanish breeds, one Ecuadorian population (Chusca Lojana), four exotic breeds, and one undefined genotype group. Genetic diversity parameters, population structure, genetic relationships, and spatial genetic patterns were evaluated through a combination of population genetic and spatial analyses. Results: The locally adapted populations showed considerable levels of genetic diversity, with the Spanish (H-o = 0.629; H-e = 0.685) and Ecuadorian (H-o = 0.628; H-e = 0.704) populations displaying higher diversity than the Brazilian populations (H-o = 0.583; H-e = 0.628). Significant genetic differentiation was observed among geographic groups. A strong and significant correlation between genetic and geographic distances was detected when all local populations were considered (r = 0.77; R-2 = 0.59; p < 0.001), as well as when only Brazilian populations were analyzed (r = 0.65; R-2 = 0.43; p = 0.0075). Spatial analyses further identified potential genetic barriers that may restrict gene flow among certain populations. Conclusions: These findings suggest that geographic isolation plays an important role in shaping the genetic structure of locally adapted goat populations, while historical connections among Iberian and South American populations may also contribute to the observed genetic relationships. The integration of genetic and spatial information provides valuable insights for understanding the evolutionary dynamics of these populations and supports the development of more effective strategies for the conservation and sustainable management of goat genetic resources.

Although previous research has demonstrated the potential benefits of agricultural livestock activities for biodiversity conservation, the ecological role of camels in productive environments-particularly their influence on surrounding wild animal diversity-remains poorly understood. The present study analyses patterns of wild animal species richness around camel livestock farms in the Canary Islands (Spain) using cartographic data and discriminant analysis. The Canary Islands host the largest population of the endangered Canarian Camel, the only European camel breed. Results from discriminant analyses confirmed significantly (Pillai trace p < 0.05) higher richness of wild animal species within a 1 km radius of camel farms, particularly among mammals, birds, arachnids, and molluscs. Officially protected species of wild mammals, birds, reptiles, insects, and molluscs also exhibit increased richness in this influence zone. Additionally, bioclimatic factors significantly (Pillai trace p < 0.05) influence the spatial patterns of wild animal species. These findings suggest that the sustainable management of Canarian Camel farms could support the preservation of local agroecosystems and the survival of coexisting wild animal species, reinforcing their potential ecological role.

The present study aimed to determine the influence of egg weight and shape index on the internal quality of turkey eggs. To this aim, a total of 197 turkey eggs were measured including external and internal egg quality traits. Different egg categories were built attending to the weight terciles of the sample and the commercial shape index standards. A Discriminant Canonical Analysis (DCA) was performed using these categories as dependent variables, while the internal quality attributes acted as explanatory variables. The yolk percentage was the only variable reporting multicollinearity and was therefore removed from further analysis. The Pillai’s trace criterion was significative (p < 0.05) and validated the performance of the DCA. Moreover, the cross-validation test reported a high accuracy of correct assignments (95.88 %), which validated the applicability of the statistic model. The diameter and lightness of the yolk, the proportion of shell in the overall weight, and the height and pH of the albumen were the variables reporting discriminatory ability across weight and shape index groups. Similar associations between the internal quality and egg weight were found in the literature, while the connections with the shape index were less frequent. The present work eases the efficient determination of increased-quality eggs from their external appearance. However, quality perception varies with the consumer preferences of each market. Therefore, heavier eggs will offer larger yolks and reduced shell percentage in weight, while lighter and rounder eggs will tend to present darker yolks, with taller albumen and lower gas exchange during storage.

Due to the lack of research in free-range poultry, the influence of environmental factors on laying remains underdeveloped nowadays. Therefore, the present study aimed to determine the influence of meteorological events and the moon cycle on the weight and shape index of turkey eggs produced in alternative systems. To this aim, 194 eggs laid by Andalusian turkey hens raised outdoors were collected and daily measured for 14 months. Seven categories were obtained attending to their weight (heavy, medium, and light) and shape index (sharp, standard, and round), and performed as dependent variables in the discriminant canonical analysis (DCA). As explanatory variables, meteorological and moon cycle features were collected from repositories consulting the day before each egg was laid. Minimum pressure, moon phase, and maximum temperature reported multicollinearity problems (VIF > 5.0) and were removed from further analysis. Moderate correlations among climatic variables influencing egg quality were found and ranged from + 0.574 to – 0.537, involving maximum gust speed in both cases. Variables exhibiting explanatory power in the DCA were sunshine hours (λ = 0.691, F = 13.950), minimum temperature (λ = 0.874, F = 4.476), and maximum gust speed (λ = 0.944, F = 1.841). Results evidence alternative productions present a different environmental exposure than indoors, as well as highlight the suitability of this breed for free range systems.

Linear appraisal scales focus on the qualitative assessment of specific traits along a standardized scale across animal populations. By minimizing errors and optimizing measurement procedures, these scales streamline resource allocation, saving time and costs for livestock managers and breeders, thereby facilitating informed breeding decisions and promoting genetic progress. A total of 130 adult, healthy dromedary camels (Camelus dromedarius) of the Canarian Camel breed were evaluated across three representative locations in Spain (Huelva, Almería, and Fuerteventura). The study aimed to optimize and validate a linear appraisal system (LAS) for assessing zoometric traits in dromedaries. Principal Component Analysis (PCA) revealed that none of the zoometric variables considered were discardable due to their ability to explain variability in the sample. However, some zoometric variables lacked statistically significant representation across one or more levels within the linear appraisal scale. Therefore, a proposal for optimizing the linear appraisal scale involves reducing the number of initially proposed linear categories for these variables. Correlation analysis revealed high internal consistency (Intraclass Correlation Coefficient (ICC) ≥ 0.7), confirming significant agreement between zoometric variability-based scales and linear appraisal traits, thus validating the LAS. Notably, interobserver reliability varied across zoometric traits, with lower agreement (ICC < 0.5) observed in apical body regions, which are more prone to movement during evaluation. Overall, our findings underscore the potential of LAS as a robust tool for zoometric evaluation in dromedary camels, aiding in the selection of superior individuals for breeding. In this context, the LAS provides a valuable framework to improve phenotypic characterization of dromedary camels, a livestock species of growing global interest due to its resilience to climate change and its longstanding role as a socio-economic cornerstone in arid and semi-arid production systems.

Contemporary research in animal cognition has expanded our understanding of non-human intelligence, yet behavioural and cognitive traits in dromedary camels remain largely unexplored. This study pioneers the empirical assessment of cognitive performance and variability in dromedaries (Camelus dromedarius) using a comparative psychometric framework adapted from human and animal cognition protocols. A total of 130 Canarian dromedaries were evaluated across thirteen cognitive traits, with individual performance indices calculated through a mental age-based model. Results revealed substantial interindividual variability in cognitive performance, with key modulating factors including group dependence, docility, and concentration. Animals with higher herd affiliation displayed above-average scores, suggesting the presence of a collective intelligence (c-factor), while independent individuals exhibited better memory and perseverance but lower attentional stability. Additional influences such as sex, sex neutering status, handler interaction, and flock size significantly shaped cognitive profiles, whereas phenotypic traits like eye and coat color had only subtle effects. These findings support the integration of cognitive assessment into camel selection schemes, with implications for animal welfare, training efficiency, and functional specialization in tourism, assisted services, and labor contexts.

Standardisation of the sperm cryopreservation technique in cockerels plays a key role, with sperm concentration being an important factor to determine. The present study aims to determine which sperm concentration provides the best post-thawing semen quality results. Semen was extracted from 16 males belonging to the Utrerana poultry breed, making a pool with the quality ejaculates for each day of semen extraction, totalling 27 replicates of which 15 were frozen at 500 x 106 spz/straw and 12 at 250 x 106 spz/straw. From each replicate, four samples are thawed to evaluate the following semen quality parameters: motility, morphology, membrane functionality (HOST), sperm viability, acrosome integrity, lipid peroxidation (LPO), reactive oxygen species (ROS), and glutathione. By means of a canonical discriminant analysis, it was determined that HOST (Lambda = 0.484, F = 113.193), straight-line velocity (VSL) (Lambda = 0.613, F = 66.963), straightness (STR) (Lambda = 0.697, F = 46.086), sperm viability (Lambda = 0.707, F = 43.860), and ROS (Lambda = 0.876, F = 15.073) were the variables that provided the most information. According to the descriptive statistics, there are better results when freezing at 250 x 106 spz/straw for HOST (70.391 vs. 50.620), STR (76.917 vs. 72.017), sperm viability (44.880 vs. 27.638), and ROS (1019.042 vs. 1139.350). On the contrary, the VSL variable showed more favourable values when increasing the concentration to 500 x 106 spz/straw (41.375 vs. 32.652). Thus, generally, we can conclude that a low freezing sperm concentration leads to better quality results in thawed rooster semen.