Productive behavior in growing kid goats and methane production with the inclusion of chokecherry leaf (Prunus salicifolia)

Weighted gene co-expression network analysis reveals genes related to growth performance in Hu sheep

Hu sheep are a unique breed in our country with great reproductive potential, the extent of whose breeding has been steadily rising in recent years. The study subjects in this experiment were 8-month-old Hu sheep (n = 112). First of all, the growth performance, slaughter performance and meat quality of their eye muscle quality were assessed, meanwhile their live weight, carcass weight, body length, body height, chest circumference, chest depth and tube circumference were respectively 33.81 ± 5.47 kg, 17.43 ± 3.21 kg, 60.36 ± 4.41 cm, 63.25 ± 3.88 cm, 72.03 ± 5.02 cm, 30.70 ± 2.32 cm and 7.36 ± 0.56 cm, with a significant difference between rams and ewes (P < 0.01). Following that, transcriptome sequencing was done, and candidate genes related to growth performance were identified using the weighted co-expression network analysis (WGCNA) approach, which was used to identified 15 modules, with the turquoise and blue modules having the strongest association with growth and slaughter performance, respectively. We discovered hub genes such as ARHGAP31, EPS8, AKT3, EPN1, PACS2, KIF1C, C12H1orf115, FSTL1, PTGFRN and IFIH1 in the gene modules connected with growth and slaughter performance. Our research identifies the hub genes associated with the growth and slaughter performance of Hu sheep, which play an important role in their muscle growth, organ and cartilage development, blood vessel development and energy metabolic pathways. Our findings might lead to the development of potentially-useful biomarkers for the selection of growth and slaughterer performance-related attributes of sheep and other livestock.

Quantifying the Impact of Different Dietary Rumen Modulating Strategies on Enteric Methane Emission and Productivity in Ruminant Livestock: A Meta-Analysis

A meta-analysis was conducted with an aim to quantify the beneficial effects of nine different dietary rumen modulating strategies which includes: the use of plant-based bioactive compounds (saponin, tannins, oils, and ether extract), feed additives (nitrate, biochar, seaweed, and 3-nitroxy propanol), and diet manipulation (concentrate feeding) on rumen fermentation, enteric methane (CH4) production (g/day), CH4 yield (g/kg dry matter intake) and CH4 emission intensity (g/kg meat or milk), and production performance parameters (the average daily gain, milk yield and milk quality) of ruminant livestock. The dataset was constructed by compiling global data from 110 refereed publications on in vivo studies conducted in ruminants from 2005 to 2023 and anlayzed using a meta-analytical approach.. Of these dietary rumen manipulation strategies, saponin and biochar reduced CH4 production on average by 21%. Equally, CH4 yield was reduced by 15% on average in response to nitrate, oils, and 3-nitroxy propanol (3-NOP). In dairy ruminants, nitrate, oils, and 3-NOP reduced the intensity of CH4 emission (CH4 in g/kg milk) on average by 28.7%. Tannins and 3-NOP increased on average ruminal propionate and butyrate while reducing the acetate:propionate (A:P) ratio by 12%, 13.5% and 13%, respectively. Oils increased propionate by 2% while reducing butyrate and the A:P ratio by 2.9% and 3.8%, respectively. Use of 3-NOP increased the production of milk fat (g/kg DMI) by 15% whereas oils improved the yield of milk fat and protein (kg/d) by 16% and 20%, respectively. On the other hand, concentrate feeding improved dry matter intake and milk yield (g/kg DMI) by 23.4% and 19%, respectively. However, feed efficiency was not affected by any of the dietary rumen modulating strategies. Generally, the use of nitrate, saponin, oils, biochar and 3-NOP were effective as CH4 mitigating strategies, and specifically oils and 3-NOP provided a co-benefit of improving production parameters in ruminant livestock. Equally concentrate feeding improved production parameters in ruminant livestock without any significant effect on enteric methane emission. Therefore, it is advisable to refine further these strategies through life cycle assessment or modelling approaches to accurately capture their influence on farm-scale production, profitability and net greenhouse gas emissions. The adoption of the most viable, region-specific strategies should be based on factors such as the availability and cost of the strategy in the region, the specific goals to be achieved, and the cost-benefit ratio associated with implementing these strategies in ruminant livestock production systems.

Identification of body size characteristic points based on the Mask R-CNN and correlation with body weight in Ujumqin sheep

The measurements of body size data not only reflect the physical fitness, carcass structure, excellent growth condition, and developmental relationship among tissues and organs of animals but are also critical indicators to measure the growth and development of sheep. Computer vision-based body size identification is a non-contact and stress-free method. In this study, we analyzed different body size traits (height at wither, body slanting length, chest depth, chest circumference, shank circumference, hip height, shoulder width, and rump width) and the body weight of 332 Ujumqin sheep and significant correlations (P &lt; 0.05) were obtained among all traits in Ujumqin sheep. Except for shoulder width, rump width, and shank circumference, all were positively correlated, and the effect of sex on Ujumqin sheep was highly significant. The main body size indexes affecting the body weight of rams and ewes were obtained through stepwise regression analysis of body size on body weight, in order of chest circumference, body slanting length, rump width, hip height, height at wither, and shoulder width for rams and body slanting length, chest circumference, rump width, hip height, height at wither and shoulder width for ewes. The body slanting length, chest circumference, and hip height of ewes were used to construct prediction equations for the body weight of Ujumqin sheep of different sexes. The model's prediction accuracy was 83.9% for the rams and 79.4% for ewes. Combined with a Mask R-CNN and machine vision methods, recognition models of important body size parameters of Ujumqin sheep were constructed. The prediction errors of body slanting length, height at wither, hip height, and chest circumference were ~5%, chest depth error was 9.63%, and shoulder width, rump width, and shank circumference errors were 14.95, 12.05, and 19.71%, respectively. The results show that the proposed method is effective and has great potential in precision management.