In equids, health and welfare depend on body composition. A growing number of equids are now used as leisure and companion animals, and often found overfeed. The need for a close monitoring of body fatness led to the search for tools allowing a rapid and non-invasive estimation of fatness. This study intends to assess real-time ultrasonography (RTU) usefulness in establishing a relationship between ultrasound measures of subcutaneous fat–plus–skin thickness (SF-Skin) and body condition score (BCS) in horses and donkeys. Forty-three healthy animals (16 donkeys and 27 horses) were used in this study to generate 95 records (RTU and BCS pairs), in multiple RTU sessions for 2 years. Using visual appraisal and palpation, BCS was graded in a 1–9 points scale. Real-time ultrasonography images were taken using a 7.5 MHz linear transducer, placed perpendicular to the backbone, over the 3rd lumbar vertebra. ImageJ was used to measure the SF-Skin on RTU images. The relation between BCS and SF-Skin measurements was tested by linear and polynomial regression analysis. The BCS values were similar in horses (5.50; from 3 to 8 points) and donkeys (5.14; from 3 to 7 points). The SF-Skin measures show a similar trend (a mean of 7.1 and 7.7 mm in horses and donkeys, respectively). A polynomial regression among BCS and SF-Skin explained 92 and 77 % of the variation in donkeys and horses respectively. The coefficient of determination was considerably higher for the regression developed for donkeys compared with that of horses (R2 = 0.92 vs. 0.77, respectively), which reduced the accuracy of the method in horses. Both the linear and polynomial models tested show a strong relationship among BCS and SF-Skin for donkeys (R2 > 0.91; P < 0.01) and horses (R2 > 0.74; P < 0.01), despite that the extremes for BCS did not existed in our sample. Our results showed the potential RTU usefulness to monitor body fat in equids. Using a high-frequency transducer and RTU together with image analysis allowed the identification of small SF-skin variations. This report will support further studies on the relationships between SF-Skin and BCS, particularly in extreme BCS scores.

The donkey breed Asinina de Miranda, with fewer than 1000 breeding females, is in danger of extinction. The objectives of this study were to predict the progression of the breed under present management and identify determinants for survival, by means of a population viability analysis program, in order to suggest suitable management strategies. The simulation showed a high risk of extinction. The most critical factor for breed survival was the percentage of females breeding per year, but the actual percentage needed depended on the carrying capacity of the breed. Reducing female mortality and age at production of first offspring, assuring registration in the Studbook, and tracking the foals will significantly foster this donkey breed’s recovery and maintenance. The breed comprised a potentially reproductive population of 589 individuals; however, just 54.1% of the adult females registered in the Studbook ever foaled, and of these 62.7% foaled just once. The overall neonatal mortality for the first month of life was 8.92% and was lower in females (6.51%) than in males (12.0%) (P = 0.028). Neonatal mortality was unevenly distributed throughout the year, with lower mortality rates recorded in February–May and October–November, and higher mortality rates in June–September and again in December–January. The neonatal foal mortality rate was lower with females aged 5–15 years (8.06%) than those younger than 4 years (10.3%) or older than 16 years (14.1%) at foaling.

The aim of the present study is to define and compare the predictive power of two different Bayesian models for donkey sperm quality after the evaluation of linear and combined testicular biometry indices and their relationship with age and body weight (BW). Testicular morphometry was ultrasonographically obtained from 23 donkeys (six juveniles and 17 adults), while 40 ejaculates from eight mature donkeys were analyzed for sperm output and quality assessment. Bayesian linear regression analyses were considered to build two statistical models using gel-free volume, concentration, total sperm number, motility, total motile sperm, and morphology as dependent variables. Predictive model 1 comprised the covariate of age and the independent factors testicular measurements (length, height and width), while model 2 included the covariate of age and the factors of BW, testicular volume, and gonadosomatic ratio. Although goodness-of-fit was similar, the combination of predictors in model 1 evidenced higher likelihood to predict gel-free volume (mL), concentration (×10⁶/mL), and motility (%). Alternatively, the combination of predictors in model 2 evidenced higher predictive power for total sperm number (×10⁹), morphologically normal spermatozoa (%), and total motile sperm count (×10⁹). The application of the present models may be useful to gather relevant information that could be used hereafter for assisted reproductive technologies.