Background Bibliometric analysis plays an essential role in scientific research. However, some research areas still have several gaps. This is also the case in the animal sciences. Therefore, the status of current studies can be analyzed to provide guidance for future research through scientific studies that will be conducted. The aim of this study was to perform a bibliometric analysis of goat breeding to evaluate current trends, gaps in knowledge, and future perspectives. Methods A large dataset was extracted from the Web of Science (WoS), a popular database for scientific studies. According to the WoS, we used 1000 scientific papers (1991–2023) that contained articles, book chapters, data papers, proceedings papers, meeting abstracts, and notes. All bibliometric analyses were performed using the bibliometrix package. Results and Conclusions In 2023, there were 34 studies available. Average citations per year showed that the highest average citation count occurred in 2006 (31.35). In total, 265 scientific journals in the WoS database have published goat breeding studies. Small Ruminant Research led to the remaining 145 articles. In conclusion, scientific studies on goat breeding have become popular in recent years. The authors may gravitate towards popular journals in the future.

In Lepelle-Nkumbi Local Municipality of South Africa, 200 none-descript indigenous goats ranging in age from one to five years were the subjects of a study that compared the live body weight predictions made by stepwise linear regression, Classification Regression Tree (CART), and Multivariate Adaptive Splines (MARS) models. Several bodily measurements, such as canonical circumference (CC), sternum height (SH), body length (BL), ear length (EL), head length (HL), head width (HW), rump length (RL), rump height (RH), and rump width (RW). The evaluation criteria included the root mean square error (RMSE), coefficient of determination (R²), to decide which model was the best. According to the results, CART outperformed the others, obtaining the lowest RMSE (3.65) and the greatest R² (0.80). The stepwise regression model outperformed data mining algorithms in male goats. According to the study, CART is a useful statistical technique for defining requirements for producing indigenous goats that are not very special. In addition, when predicting live body weight from body measuring features, the stepwise regression model should be considered.

Data mining algorithms have been performed to reveal the factors that can be used to enhance live body weight and egg weight during chicken breeding. This work was conducted to systematically review the published articles on the use of data mining algorithms in chicken breeding. ScienceDirect, Web of Science, PubMed, Google Scholar and were used for searching articles. Using the combination of chicken or chicken breeding, data mining algorithm or decision tree, body weight and egg weight as keywords. The results indicated that 8 articles were included from 120 articles were found from searching. The 8 included articles were published from 2016 to 2021 and most of them were originated from South Africa (n=3) followed by Turkey (n=2) with. CHAID as the most used data mining algorithm (n=5) followed by CART (n=4). Out of 8 included articles, 6 of them used coefficient of determination (R²) as the selection criteria and CART was found as the best model followed by the CHAID model. It is concluded that CART followed by CHAID data mining algorithms are the recommended models that might be used for improving egg production and growth performance of chickens.

Savanna goats is one of the South African commercial meat breeds. This study was conducted to predict body weight from body volume (BV), heart girth (HG) and body length (BL). A total of 139 savanna goats of the age between 1 and 5 years old of different sex (male and female) were used in the study for collection of body weight, body length and heart girth. The animals were kept under semi-intensive production system, where they were supplemented in the afternoon. Body volume was derived using cylinder volume formula from body length and heart girth as the components of model. R-studio software was employed for Pearson correlation matrix to assess the association between body weight, body length, heart girth and body volume. Simple linear regression was used to establish model to predict body weight. Pearson correlation results indicated that BW had a highly statistical significant (p<0.01) correlation with HG (r = 0.90), BV (r = 0.84) and BL (r = 0.66), respectively. Regression model findings indicated that HG had highest coefficient of determination (R ² = 0.81) and lowest mean square error (MSE = 24.85), and BV indicated highest coefficient of determination (R ² = 0.76) and low mean square error of (MSE = 35.07) while BL indicated lowest coefficient determination of (R ² = 0.45) and high mean square error (MSE = 70.80). In conclusion, correlation result suggests that by improving HG, BV and BL will result in improving BW of Savanna goats. Simple linear regression suggest that HG and BV can be used to estimate BW of Savanna goats.

Chicken layers are valued for their ability to produce eggs, an essential component of the human diet. Egg characteristics are influenced by genetic factors such as the growth differentiation factor‐9 (GDF‐9) gene, which plays a vital role in follicular development and ovulation. Variations in the GDF‐9 gene can lead to ovulation dysfunction, infertility, and reduced egg quality and production. This systematic review explored the genetic variation of the chicken GDF‐9 gene and its association with egg characteristics. Following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines, databases including ScienceDirect, Google Scholar, PubMed, and Web of Science were used to search for articles using combinations of keywords: “chickens,” “GDF‐9 gene,” “genetic variation,” and “egg characteristics.” A total of 106 articles were retrieved, and five met the inclusion criteria. The reviewed studies identified several single nucleotide polymorphisms (SNPs) within the GDF‐9 gene—namely g.171515C>T, g.171530A>G, g.171529G>A, g.171554C>T, g.171577A>G, g.171600C>T, g.171541A>G, g.171560T>C, and g.171589C>T. Although no identical SNPs were shared among all breeds, these variants were associated with key egg traits such as age at first egg, egg number, and egg weight. The findings suggest that GDF‐9 gene polymorphisms hold strong potential for use in marker‐assisted and genomic selection to enhance reproductive performance and egg‐laying efficiency in chickens. However, the current findings are primarily based on conventional genetic association studies, and higher resolution transcriptomic approaches, such as single‐cell RNA sequencing, are needed to functionally validate these associations and clarify the molecular mechanisms underlying GDF‐9 regulation in reproductive traits. Further research using genetically diverse populations and incorporating gene expression analysis is recommended to validate these associations and strengthen the application of GDF‐9 in poultry breeding.

Environmental stresses negatively affect feed intake in chickens, leading to reduced growth rates and significant economic losses. This review examined the molecular mechanisms of stress-induced loss of appetite, focusing on the hypothalamic regulation of appetite-associated neuropeptides, the role of the HPA axis, elevated CORT levels, and stress-induced inflammatory cytokines that inhibit appetite-stimulating signals. Current mitigation strategies, such as cooling systems, nutritional supplements, early-life programs and genetic selection using QTL mapping, have been shown to provide varying levels of effectiveness. Limited understanding of how multiple stressors act in concert at the cellular level in natural environments is a major limitation. Furthermore, the majority of studies focus on acute stress, while low-grade chronic stress models are underrepresented despite their commercial importance. A significant shortcoming is the absence of validated non-invasive biomarkers to predict stress-induced feed reductions before production losses begin. This review examines the molecular neuroendocrine mechanisms of stress-induced anorexia and the available mitigation strategies, highlighting the disconnect between mechanistic understanding and current interventions, and emphasizing the need for mechanism-based proactive measures to sustainably improve feed intake and productivity in stressed chickens. Therefore, future research should focus on integrated multi-stress frameworks and the evaluation of the lasting epigenetic effects of early-life interventions in chickens.