Sobhy Mohamed Sallam

Date pits (DPs) are a rich and economical source of dietary energy, and they show promise as an alternative to expensive concentrates in cattle feeding. This study examined the effect of replacing yellow corn grain with different levels of date pits treated with Aspergillus parasiticus on in vitro rumen fermentation characteristics, nutrient degradability, methanogenic community, and methane production using a semi-automatic system of gas production (GP). In the in vitro experiment, a basal diet (50% concentrate mixture and 50% clover hay) was incubated without date pits (control) and with replacement ratios (25, 50, 75, and 100%) of fungi-degraded date pits (FDDPs). Linear and quadratic reductions ( p = 0.001) in gas production (GP), truly degraded dry matter (TDDM), and truly degraded organic matter (TDOM) values were observed ( p = 0.001), with increasing levels of FDDP supplementation. However, FDDP supplementation did not affect CH ₄ , PF, total protozoa, NH _3, or the molar proportions of the main short-chain fatty acids (SCFAs). The majority of the identified methanogen sequences grouped within the Methanobrevibacter lineage, according to phylogenetic analysis. Bootstrap values ranging from 39 to 67% confirmed the observed clustering patterns and demonstrated the evolutionary relatedness of these sequences; there were no notable differences in methane production when using varying levels of FDDP. It might be concluded from the results of this study that fermented date pits are a promising alternative to corn grain, with no detrimental effects on rumen fermentation characteristics and the methanogenic community in the feeding of ruminants.

Early and accurate identification of mastitis-associated inflammation is critical to increasing animal comfort, reducing financial losses, and enhancing milk quality. Although infrared thermography (IRT) has emerged as a viable non-invasive screening technique, the relative efficacy of convolutional neural networks (CNNs) and Vision Transformers (ViTs) for automated CMT-status classification from thermal udder pictures is still unknown. This work carefully analyzed seven ImageNet-pretrained deep learning architectures using 976 thermal udder images (708 healthy and 268 mastitic images) from 488 Holstein cows (354 healthy cows, 708 images; 134 mastitic cows, 268 images), including two Vision Transformer models (ViT-B/16 and Swin-Tiny) and five CNN models (ResNet-50, DenseNet-121, EfficientNet-B0, MobileNetV2, Inception-V3). Before training, pictures were preprocessed using contrast-limited adaptive histogram equalization (CLAHE), scaled to 224 × 224 pixels, and divided using cow-oriented grouping intended to reduce animal-level data leakage (this grouping could not be independently verified against a ground-truth cow roster; see Limitations). Each model was developed from start to finish and evaluated using an independent hold-out test set and five-fold animal-level cross-validation. DenseNet-121 had the greatest results on the hold-out test set, with an accuracy of 82.2%, an AUC of 0.922, a sensitivity of 90.0%, and a specificity of 79.2%. Additionally, it achieved the highest cross-validation performance (mean AUC = 0.914 ± 0.019). According to statistical analysis, DenseNet-121 was statistically indistinguishable from Inception-V3 under both tests and from ResNet-50 under the more conservative corrected resampled t-test, but significantly outperformed EfficientNet-B0, MobileNetV2, and both Vision Transformers (p < 0.05), while all CNN models outperformed both Vision Transformer models (p < 0.05). Explainable artificial intelligence (XAI) investigation utilizing Grad-CAM corroborated the biological plausibility of the learnt plausibility characteristics by showing that heat patterns in the udder region had a significant impact on model predictions. Grad-CAM analysis showed that heat patterns in the udder region partly drove model predictions, though attention was occasionally influenced by background regions. The best-performing model was tested without retraining on an independent external cohort of 85 cows from a different farm. Although external performance decreased (accuracy = 60.0%; Cohen’s κ = 0.199), consistent with the expected effects of domain shift, the somatic cell count was significantly higher in CMT-positive cows (p < 0.001; AUC = 0.783), supporting the biological significance of the identified thermal abnormalities. Fine-tuned CNNs, led by DenseNet-121, delivered accurate and reproducible thermal CMT-status classification under internal cross-validation, but transportability to an independent farm was limited (external accuracy = 0.600, κ = 0.199), indicating that domain adaptation and multi-farm validation are needed before broader deployment. These findings provide strong support for the application of CNN-based deep learning in IRT-assisted CMT-status screening while highlighting the necessity for larger multicenter datasets to further evaluate transformer-based approaches. aptation and multi-farm validation are needed before broader deployment. These findings provide strong support for the application of CNN-based deep learning in IRT-assisted mastitis screening while highlighting the necessity for larger multicenter datasets to further evaluate transformer-based approaches.

Balancing maximum biomass yield with high nutritional quality and reduced environmental impact remains a primary challenge in sustainable summer forage production for livestock, a challenge intensified by competition between conventional and forage crops for agricultural land, particularly regarding planting dates and land occupation duration, which limits available livestock forage resources. To address this issue, this study evaluated the forage yield, quality, and nutritive value of teosinte (Zea Mexicana L.) under three sowing dates: 1 May, 20 May, and 10 June, and three harvest ages: 40, 55, and 70 days after sowing (DAS), under Egyptian field conditions. Results showed that delaying harvesting until 70 DAS produced the highest fresh (34.95 to 46.0 t ha⁻¹) and dry (5.38 to 6.03 t ha⁻¹) yields for the late sowing date (10 June). The in vitro gas production (GP) assay showed that GP was significantly higher for teosinte sown on May 20 and June 10 but declined progressively with advancing harvest age. The NH₃-N concentration decreased (p < 0.05) when teosinte was sown on May 1 and harvested at 40 DAS. True organic matter degradability declined for teosinte sown on May 1, May 20, and June 10 when harvested at 70 DAS, whereas it increased at harvest ages of 40 and 55 DAS for teosinte sown on May 1 and June 10. Teosinte sown on May 1 produced significantly lower methane emissions than that sown on May 20 or June 10, regardless of harvest age (40, 55, or 70 DAS). Overall, delaying harvest until 70 DAS resulted in the highest fresh and dry matter yields but significantly decreased gas production and organic matter degradability compared to earlier harvests, which require future research to optimize the utilization of teosinte.

The impact of essential oils on amino acid metabolism in relation to the rumen fermentation outcomes and adaptation to a solid diet is not well documented. Our objective was to evaluate the impact of an essential oil blend (EOB; contains cinnamaldehyde, capsicum, curcumin, and saponin) on growth performance, rumen fermentation, and serum amino acids of weaned growing calves. Sixteen weaned Holstein calves were blocked by body weight (100 ± 6 kg), and randomly allotted into: 1—CON group (n = 8; received 0% EOB), and 2—EOB group (n = 8; received 0.5 g of the EOB/Kg dry matter for 60 d, sex was used to balance the groups, so each group has 4 male and 4 female calves, but it is not the main scope of this study. Growth performance, rumen fermentation, and serum amino acids were assessed. Growth performance was not affected by treatment. Rumen pH decreased, and valeric acid, acetate, and propionate levels were increased. Serum leucine was reduced, and aspartate increased. Metabolic pathway enrichment analysis identified alanine, aspartate, and glutamate metabolism as the most affected metabolic pathway. In conclusion, supplementation with this EOB did not impact growth performance, despite its impact on rumen fermentation and blood amino acids in weaned Holstein calves. This study indicates that phytogenic supplementation may contribute to metabolic modulation during the post-weaning period through modulation of amino acid-associated metabolic pathways.

Mastitis is a major and costly dairy disease that reduces milk yield and quality and harms animal welfare. This study evaluated infrared thermography (IRT) combined with machine learning (ML) for non-invasive mastitis screening in dairy cows and explored links with biological and feeding-system variables in Egyptian farms. A total of 976 thermal udder images obtained from 488 Holstein cows were used, including 708 healthy and 268 mastitic images. Images were captured before milking, processed with CLAHE, resized to 224 × 224 pixels, and split using cow-level grouping before augmentation to prevent animal-level data leakage. The training set contained 780 original images and was augmented to a balanced 4708-image set (2354 per class), while the held-out test set remained unaugmented, with 196 original images (142 healthy and 54 mastitic). EfficientNetB3 with global average and max pooling extracted 3072 thermal features, and ten ML classifiers were evaluated. In the image-level hold-out evaluation, MLP achieved the best performance (accuracy = 86.22%, AUC = 0.9184, sensitivity = 74.07%, specificity = 90.85%), followed by SVM (accuracy = 83.67%, AUC = 0.8963). A separate group-based five-fold cross-validation yielded a more conservative AUC of 0.6812 ± 0.1323 and accuracy of 0.6244 ± 0.0642. Logistic regression analyses did not identify statistically significant associations between model predictions and somatic cell count (SCC), California Mastitis Test (CMT), blood biomarkers, or nutritional variables at p < 0.05. Ration A (Delta Misr) showed a higher observed mastitis incidence (20/40; 50.0%) than Ration B (Copenhagen; 16/45; 35.6%), but nutritional predictors were not statistically significant, indicating that farm-level confounding should be considered. Overall, IRT with ML remains a promising non-invasive screening approach, but broader multicenter datasets and independent external validation are needed before routine farm deployment.

This chapter was designed to figure out the basic knowledge about domestication, adaptation and immigration of goat breeds, with a spotlight on modern versus old strategies to enhance genetic performance along with recognizing their role in the livestock production industry with a special focus on the position adaptive selection in view of the new high technologies of investigating genome and building of selective comparison between goat breeds. Also, this chapter focused on goat production throughout the world, the vital role played by goats and the biodiversity of goat genetic resources and the special characteristics of goats under different conditions. In addition, this chapter is concerned with modern strategies to enhance goat genetic performance, including different molecular tools, besides mentioning the outcomes of utilizing advanced molecular tools in goat breeding, as well as identification of candidate genes related to important economic traits, detection of signatures of selection and quantitative trait loci, applied genome-wide association studies and the methodology of genomic selection, where the recent findings of genomic studies on goats are listed in a logical and sequential fashion.

The effect of including lemongrass extract powder (LGEP) as a phytogenic herbal extract supplementation for growing rabbits on productive performance, nutrient digestibility, blood parameters and carcase characteristics were evaluated. One hundred twenty unsexed V-Line rabbits, with 35 days age and 802 ± 47 g weight, were used. Rabbits were divided equally into four treatments to be fed on basal diet as a control diet without lemongrass inclusion (LGEP0) or with the inclusion of LGEP at 2 g (LGEP2), 4 g (LGEP4) or 6 g (LGEP6) per kg diet. Feeding LGEP did not significantly affect final body weight, weekly weight gains, feed consumption or feed conversion ratio. Moreover, no significant effect of supplementing rabbit diets with LGEP was observed on all apparent nutrient digestibility. The LGEP4 treatment decreased (P<.05) blood malondialdehyde, while LGEP contained treatments increased blood immunoglobulin G concentration as well as spleen relative weight (P<.01) compared to the control treatment. Although LGEP inclusion levels did not significantly affect productive performance traits, the studied LGEP treatments may help to enhance the anti-oxidative status and relative immune response of growing rabbits.

In vitro and in vivo experiments were conducted to investigate the potential impacts of different levels of essential oils blend (EOB 0, 400, 800 ?l/kg of total mixed ration) on total gas and methane production, as well as on rumen fermentation parameters and nutrient utilization. The in vitro assay was carried out using semi-automatic system of gas production (GP) technique. In vivo evaluation was performed using 12 mature male Barki sheep (39±3.13 kg live body weight) randomly allocated into three groups. First group (control, n=4) was fed a basal diet, the second group (EOB 0.5 , n=4) was fed the basal diet supplemented with 0.5 ml of EOB/head/d, while the third group (EOB 1.0 , n=4) received the basal diet plus 1.0 ml of EOB/head/d. The investigation included mixture of commercial essential oils (EO) by equal proprotions e.g., eucalyptus ( Eucalyptus globules ), cinnamon ( Cinnamomum cassia ), peppermint ( Mentha piperita ), thyme ( Thymus vulgaris ) and lemon ( Citrus limon ),. The chemical profiles of individual and EOB were analyzed by GC/MS . The GC/MS results revealed that the cinnamic aldehyde (100%), 1,8-cineole (95.0%), thymol (39.4%), menthol (38.6%) and dl-limonene (88.6%) were the main components in EOs of cinnamon, eucalyptus, thyme, peppermint and lemon, respectively. There were no significant effects among investigated levels of EOB on in vitro GP and methane production, short chain fatty acids (SCFA), NH 3 -N concentration and protozoa count. The truly degraded organic and dry matter slightly decreased at the highest concentration of EOB tested. In vivo inclusion of EOB 0.5 had no significant effect on dry matter intake (DMI) compared to EOB 1.0 and untreated animals. Dry and organic matter digestion coefficients were not affected by EOB supplementation, while the digestion coefficients of crude protein (CP) decreased (P0.05) neutral detergent fiber (NDF) digestion compared to EOB 1.0 and control group. Acid detergent fiber (ADF) digestion coefficient was not affected by the treatments. Total digestible nutrients (TDN) and digestible crude protein (DCP) were not affected by EOB treatments. There were no significant effects of both doses of EOB supplementation on N-balance, rumen pH, SCFA or NH 3 -N concentration. It can be concluded that EOB 0.5 decreased CP digestion in sheep without positive impacts on other nutrients utilization and ruminal fermentation patterns, which may be related to the additive effects of the combination of EO and inadequate dose supplementation in the sheep diets.