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- ItemINVESTIGATION OF SOME ESSENTIAL OILS USED IN PRESERVATION AND STORAGE OF BEEF TSIRE IN NYLON NET(2025-05)This study examines the preservative potential of essential oils extracted from Moringa (Moringa oleifera), Clove (Syzygium aromaticum), and Sesame (Sesamum indicum) for extending the shelf life of beef Tsire stored in nylon net.A 4×4 factorial arrangement in a Completely Randomized Design was used, fresh beef samples were purchased from the Animal Product Processing Laboratory of the Department of Animal Science, ABU, Zaria. Moringa, Clove, and Sesame oils were procured from a local herbal pointin ABU Zaria. The beef was sliced into uniform strips, washed, and seasoned with a standard Tsire spice mix, including groundnut cake powder, ginger, pepper, and salt. The seasoned meat was skewered and roasted at 160°C for 30 minutes to simulate traditional preparation. Three different concentrations (0.5%, 1.0%, and 1.5%) of each essential oil were prepared and applied uniformly to separate beef Tsire samples. The treated samples were stored in sterile nylon net to facilitate airflow while preventing contamination. Control samples (without essential oil treatment) were also stored in nylon net for comparison. Total viable count (TVC), yeast and mold count, and coliform bacteria count were assessed using plate count techniques on nutrient agar, potato dextrose agar, and MacConkey agar, respectively. Oxidative Stability (Lipid Oxidation): The thiobarbituric acid reactive substances (TBARS) assay was used to measure malondialdehyde (MDA) formation, an indicator of lipid oxidation. Sensory evaluation was carried out using semi-trained panelist. The beef Tsire samples were monitored for 0-72 hours, with periodic microbial and oxidative analyses to determine the effectiveness of the essential oils in prolonging freshness. The data collected were statistically analyzed using ANOVA (Analysis of Variance) to determine significant differences between treatments. Duncan’s Multiple Range Test (DMRT) was applied to compare mean values at p < 0.05. Clove oil had the highest levels of alkaloids (87.50 mg/g), total phenol (218.43 mg/g), tannins (186.99 mg TAE/g), and DPPH radical scavenging activity (61.60 μg/ml). Sesame oil also exhibited high bioactive compounds, particularly in flavonoid (189.79 mg QE/g) and tannin (176.23 mg TAE/g) content.The microbial analysis revealed that Clove oil at 1.5% concentration reduced total bacterial count by 33.7% compared to the control (105.25 × 10^6 cfu/g versus 69.75 × 10^6 cfu/g, respectively). Lipid oxidation, measured through malondialdehyde (MDA) levels, was significantly lower in Clove oil-treated samples, showing a 49.3% reduction (20.78 mg/kg) compared to untreated samples (40.95 mg/kg). Sensory evaluations indicated that Clove oil improved overall acceptability, scoring 4.5 out of 5 on a hedonic scale, while control samples scored 3.2. Throughout a 72-hour storage period, samples treated with essential oils exhibited enhanced stability, with Sesame oil maintaining the highest lipid content (24.53%) and Clove oil preserving the highest pH (6.75), indicative of delayed spoilage. The findings suggest that Clove, Moringa, and Sesame essential oils can effectively enhance the microbial safety and sensory quality of beef Tsire, providing a natural alternative for meat preservation.
- ItemESTIMATION OF PHENOTYPIC AND GENETIC PARAMETERS OF MILK YIELD, CONFORMATION AND FERTILITY TRAITS IN DAIRY CATTLE: A MULTI-GENOTYPE AND MULTI-LOCATIONAL STUDY(2017-04)This study aimed to estimate the phenotypic and genetic parameters of milk yield, conformation, and fertility traits in multi-genotype cows across diverse environments. Data were collected from six genotypes: Holstein Friesian, Friesian×Bunaji, Jersey, Jersey×Bunaji, Brown Swiss, and Simmental cows, reared on three commercial farms: Shonga Dairy Holdings in Kwara State, Integrated Dairies Limited in Plateau State, and Sebore Farm in Adamawa State, Nigeria. Milk production traits assessed included 305-day fat-corrected milk yield, daily milk yield, 100-day fat-corrected milk yield, total fat yield, total protein yield, and lactation length. Six efficiency indices were evaluated: fat-corrected milk yield per kilogram weight (FCM Kg W), per kilogram metabolic weight (FCM Kg MW), per day per kilogram weight (FCM/day/kgW), per day per kilogram metabolic weight (FCM/day/kgMW), net energy efficiency, and dairy merit. Additionally, four lactation traits (initial yield, peak yield, peak day, last day yield), body weight, seven body traits (body condition score, stature, chest width, body depth, heart girth, rump width), five udder traits (central ligament, rear udder height and width, udder clearance, teat length), and six fertility traits (age at first calving, calving interval, days open, services per conception, calving rate, herd life) were analyzed. The effects of genotype, breed improvement, and year of calving on fertility traits were also examined. Multi-trait animal models using the average information restricted maximum likelihood (AIREML) method were employed to estimate (co)variance components, with basic descriptive and regression analyses performed in R 3.0.3 and computational modeling conducted in MATLAB. The average milk production metrics were 2496.4 kg for 305-day fat-corrected milk yield, 7.2 kg/day for daily milk yield, 1549.2 kg for 100-day fat-corrected milk yield, 63.3 kg for fat yield, 58.7 kg for protein yield, and 344.9 days for lactation length. Efficiency indices included 4.8 kg FCM Kg W, 22.7 kg FCM Kg MW, 0.02 kg FCM/day/kgW, 0.07 kg FCM/day/kgMW, 42.4% net energy efficiency, and 61.8% dairy merit. Fertility traits were significantly (P<0.05) affected by genotype, breed improvement, year of calving, and their interactions. Milk production, lactation traits, and conformation traits were significantly (P<0.05) influenced by genotype and location. Heritability estimates were moderate to high for milk yield (h² = 21–44%), low to high for conformation traits (h² = 2–61%), and low to moderate for fertility traits (h² = 1–28%). Genetic and environmental correlations among milk yield, milk components, conformation, and fertility traits were less than unity across environments. Breeding value estimation accuracy ranged from moderate to high for 305-day fat-corrected milk yield and from low to high for reproductive traits. The effect of inbreeding on milk production and fertility traits was minimal overall but showed considerable severity in Jersey herds in Kwara State. All lactation models (Wood, Wilmink, Dijkstra, MilkBot, and Neural Network) effectively reconstructed the ascending, peak, and descending phases of lactation, except for the Wilmink model, which produced atypical curves for Friesian×Bunaji cows in Kwara State (Adj R² = 62%), and the Dijkstra model for Holstein Friesian cows in Adamawa State (Adj R² = 56%). The Genetic Function Algorithm (GFA) was identified as the most efficient and economical model for predicting 305-day fat-corrected milk yield in Nigerian herds (FCM305d = 1036.1 - 98.3RP + 22FY + 15.92UC - 0.07RUH; Adj R² = 0.997; RMSE = 30.07; BIC = 1997.28). Neural Network models demonstrated the highest prediction accuracy across environments, with the optimal architecture for predicting 305-day fat-corrected milk yield being a 6-2-1 multilayer perceptron using backpropagation with an 88% learning rate and 2% bias. Holstein Friesian cows showed the highest dairy merit for milk production in Plateau and Adamawa States, while Jersey cows exhibited optimal milk yield in Kwara State. These findings highlight substantial genetic variation for milk production, conformation, fertility, and lactation traits among multi-genotype cows across different environments.
- ItemESTIMATION OF PHENOTYPIC AND GENETIC PARAMETERS OF MILK YIELD, CONFORMATION AND FERTILITY TRAITS IN DAIRY CATTLE: A MULTI-GENOTYPE AND MULTI-LOCATIONAL STUDY(2017-04)This study aimed to estimate the phenotypic and genetic parameters of milk yield, conformation, and fertility traits in multi-genotype cows across diverse environments. Data were collected from six genotypes: Holstein Friesian, Friesian×Bunaji, Jersey, Jersey×Bunaji, Brown Swiss, and Simmental cows, reared on three commercial farms: Shonga Dairy Holdings in Kwara State, Integrated Dairies Limited in Plateau State, and Sebore Farm in Adamawa State, Nigeria. Milk production traits assessed included 305-day fat-corrected milk yield, daily milk yield, 100-day fat-corrected milk yield, total fat yield, total protein yield, and lactation length. Six efficiency indices were evaluated: fat-corrected milk yield per kilogram weight (FCM Kg W), per kilogram metabolic weight (FCM Kg MW), per day per kilogram weight (FCM/day/kgW), per day per kilogram metabolic weight (FCM/day/kgMW), net energy efficiency, and dairy merit. Additionally, four lactation traits (initial yield, peak yield, peak day, last day yield), body weight, seven body traits (body condition score, stature, chest width, body depth, heart girth, rump width), five udder traits (central ligament, rear udder height and width, udder clearance, teat length), and six fertility traits (age at first calving, calving interval, days open, services per conception, calving rate, herd life) were analyzed. The effects of genotype, breed improvement, and year of calving on fertility traits were also examined. Multi-trait animal models using the average information restricted maximum likelihood (AIREML) method were employed to estimate (co)variance components, with basic descriptive and regression analyses performed in R 3.0.3 and computational modeling conducted in MATLAB. The average milk production metrics were 2496.4 kg for 305-day fat-corrected milk yield, 7.2 kg/day for daily milk yield, 1549.2 kg for 100-day fat-corrected milk yield, 63.3 kg for fat yield, 58.7 kg for protein yield, and 344.9 days for lactation length. Efficiency indices included 4.8 kg FCM Kg W, 22.7 kg FCM Kg MW, 0.02 kg FCM/day/kgW, 0.07 kg FCM/day/kgMW, 42.4% net energy efficiency, and 61.8% dairy merit. Fertility traits were significantly (P<0.05) affected by genotype, breed improvement, year of calving, and their interactions. Milk production, lactation traits, and conformation traits were significantly (P<0.05) influenced by genotype and location. Heritability estimates were moderate to high for milk yield (h² = 21–44%), low to high for conformation traits (h² = 2–61%), and low to moderate for fertility traits (h² = 1–28%). Genetic and environmental correlations among milk yield, milk components, conformation, and fertility traits were less than unity across environments. Breeding value estimation accuracy ranged from moderate to high for 305-day fat-corrected milk yield and from low to high for reproductive traits. The effect of inbreeding on milk production and fertility traits was minimal overall but showed considerable severity in Jersey herds in Kwara State. All lactation models (Wood, Wilmink, Dijkstra, MilkBot, and Neural Network) effectively reconstructed the ascending, peak, and descending phases of lactation, except for the Wilmink model, which produced atypical curves for Friesian×Bunaji cows in Kwara State (Adj R² = 62%), and the Dijkstra model for Holstein Friesian cows in Adamawa State (Adj R² = 56%). The Genetic Function Algorithm (GFA) was identified as the most efficient and economical model for predicting 305-day fat-corrected milk yield in Nigerian herds (FCM305d = 1036.1 - 98.3RP + 22FY + 15.92UC - 0.07RUH; Adj R² = 0.997; RMSE = 30.07; BIC = 1997.28). Neural Network models demonstrated the highest prediction accuracy across environments, with the optimal architecture for predicting 305-day fat-corrected milk yield being a 6-2-1 multilayer perceptron using backpropagation with an 88% learning rate and 2% bias. Holstein Friesian cows showed the highest dairy merit for milk production in Plateau and Adamawa States, while Jersey cows exhibited optimal milk yield in Kwara State. These findings highlight substantial genetic variation for milk production, conformation, fertility, and lactation traits among multi-genotype cows across different environments.
- ItemESTIMATION OF PHENOTYPIC AND GENETIC PARAMETERS OF MILK YIELD, CONFORMATION AND FERTILITY TRAITS IN DAIRY CATTLE: A MULTI-GENOTYPE AND MULTI-LOCATIONAL STUDY(2017-04)This study aimed to estimate the phenotypic and genetic parameters of milk yield, conformation, and fertility traits in multi-genotype cows across diverse environments. Data were collected from six genotypes: Holstein Friesian, Friesian×Bunaji, Jersey, Jersey×Bunaji, Brown Swiss, and Simmental cows, reared on three commercial farms: Shonga Dairy Holdings in Kwara State, Integrated Dairies Limited in Plateau State, and Sebore Farm in Adamawa State, Nigeria. Milk production traits assessed included 305-day fat-corrected milk yield, daily milk yield, 100-day fat-corrected milk yield, total fat yield, total protein yield, and lactation length. Six efficiency indices were evaluated: fat-corrected milk yield per kilogram weight (FCM Kg W), per kilogram metabolic weight (FCM Kg MW), per day per kilogram weight (FCM/day/kgW), per day per kilogram metabolic weight (FCM/day/kgMW), net energy efficiency, and dairy merit. Additionally, four lactation traits (initial yield, peak yield, peak day, last day yield), body weight, seven body traits (body condition score, stature, chest width, body depth, heart girth, rump width), five udder traits (central ligament, rear udder height and width, udder clearance, teat length), and six fertility traits (age at first calving, calving interval, days open, services per conception, calving rate, herd life) were analyzed. The effects of genotype, breed improvement, and year of calving on fertility traits were also examined. Multi-trait animal models using the average information restricted maximum likelihood (AIREML) method were employed to estimate (co)variance components, with basic descriptive and regression analyses performed in R 3.0.3 and computational modeling conducted in MATLAB. The average milk production metrics were 2496.4 kg for 305-day fat-corrected milk yield, 7.2 kg/day for daily milk yield, 1549.2 kg for 100-day fat-corrected milk yield, 63.3 kg for fat yield, 58.7 kg for protein yield, and 344.9 days for lactation length. Efficiency indices included 4.8 kg FCM Kg W, 22.7 kg FCM Kg MW, 0.02 kg FCM/day/kgW, 0.07 kg FCM/day/kgMW, 42.4% net energy efficiency, and 61.8% dairy merit. Fertility traits were significantly (P<0.05) affected by genotype, breed improvement, year of calving, and their interactions. Milk production, lactation traits, and conformation traits were significantly (P<0.05) influenced by genotype and location. Heritability estimates were moderate to high for milk yield (h² = 21–44%), low to high for conformation traits (h² = 2–61%), and low to moderate for fertility traits (h² = 1–28%). Genetic and environmental correlations among milk yield, milk components, conformation, and fertility traits were less than unity across environments. Breeding value estimation accuracy ranged from moderate to high for 305-day fat-corrected milk yield and from low to high for reproductive traits. The effect of inbreeding on milk production and fertility traits was minimal overall but showed considerable severity in Jersey herds in Kwara State. All lactation models (Wood, Wilmink, Dijkstra, MilkBot, and Neural Network) effectively reconstructed the ascending, peak, and descending phases of lactation, except for the Wilmink model, which produced atypical curves for Friesian×Bunaji cows in Kwara State (Adj R² = 62%), and the Dijkstra model for Holstein Friesian cows in Adamawa State (Adj R² = 56%). The Genetic Function Algorithm (GFA) was identified as the most efficient and economical model for predicting 305-day fat-corrected milk yield in Nigerian herds (FCM305d = 1036.1 - 98.3RP + 22FY + 15.92UC - 0.07RUH; Adj R² = 0.997; RMSE = 30.07; BIC = 1997.28). Neural Network models demonstrated the highest prediction accuracy across environments, with the optimal architecture for predicting 305-day fat-corrected milk yield being a 6-2-1 multilayer perceptron using backpropagation with an 88% learning rate and 2% bias. Holstein Friesian cows showed the highest dairy merit for milk production in Plateau and Adamawa States, while Jersey cows exhibited optimal milk yield in Kwara State. These findings highlight substantial genetic variation for milk production, conformation, fertility, and lactation traits among multi-genotype cows across different environments.
- ItemESTIMATION OF PHENOTYPIC AND GENETIC PARAMETERS OF MILK YIELD, CONFORMATION AND FERTILITY TRAITS IN DAIRY CATTLE: A MULTI-GENOTYPE AND MULTI-LOCATIONAL STUDY(2017-04)This study aimed to estimate the phenotypic and genetic parameters of milk yield, conformation, and fertility traits in multi-genotype cows across diverse environments. Data were collected from six genotypes: Holstein Friesian, Friesian×Bunaji, Jersey, Jersey×Bunaji, Brown Swiss, and Simmental cows, reared on three commercial farms: Shonga Dairy Holdings in Kwara State, Integrated Dairies Limited in Plateau State, and Sebore Farm in Adamawa State, Nigeria. Milk production traits assessed included 305-day fat-corrected milk yield, daily milk yield, 100-day fat-corrected milk yield, total fat yield, total protein yield, and lactation length. Six efficiency indices were evaluated: fat-corrected milk yield per kilogram weight (FCM Kg W), per kilogram metabolic weight (FCM Kg MW), per day per kilogram weight (FCM/day/kgW), per day per kilogram metabolic weight (FCM/day/kgMW), net energy efficiency, and dairy merit. Additionally, four lactation traits (initial yield, peak yield, peak day, last day yield), body weight, seven body traits (body condition score, stature, chest width, body depth, heart girth, rump width), five udder traits (central ligament, rear udder height and width, udder clearance, teat length), and six fertility traits (age at first calving, calving interval, days open, services per conception, calving rate, herd life) were analyzed. The effects of genotype, breed improvement, and year of calving on fertility traits were also examined. Multi-trait animal models using the average information restricted maximum likelihood (AIREML) method were employed to estimate (co)variance components, with basic descriptive and regression analyses performed in R 3.0.3 and computational modeling conducted in MATLAB. The average milk production metrics were 2496.4 kg for 305-day fat-corrected milk yield, 7.2 kg/day for daily milk yield, 1549.2 kg for 100-day fat-corrected milk yield, 63.3 kg for fat yield, 58.7 kg for protein yield, and 344.9 days for lactation length. Efficiency indices included 4.8 kg FCM Kg W, 22.7 kg FCM Kg MW, 0.02 kg FCM/day/kgW, 0.07 kg FCM/day/kgMW, 42.4% net energy efficiency, and 61.8% dairy merit. Fertility traits were significantly (P<0.05) affected by genotype, breed improvement, year of calving, and their interactions. Milk production, lactation traits, and conformation traits were significantly (P<0.05) influenced by genotype and location. Heritability estimates were moderate to high for milk yield (h² = 21–44%), low to high for conformation traits (h² = 2–61%), and low to moderate for fertility traits (h² = 1–28%). Genetic and environmental correlations among milk yield, milk components, conformation, and fertility traits were less than unity across environments. Breeding value estimation accuracy ranged from moderate to high for 305-day fat-corrected milk yield and from low to high for reproductive traits. The effect of inbreeding on milk production and fertility traits was minimal overall but showed considerable severity in Jersey herds in Kwara State. All lactation models (Wood, Wilmink, Dijkstra, MilkBot, and Neural Network) effectively reconstructed the ascending, peak, and descending phases of lactation, except for the Wilmink model, which produced atypical curves for Friesian×Bunaji cows in Kwara State (Adj R² = 62%), and the Dijkstra model for Holstein Friesian cows in Adamawa State (Adj R² = 56%). The Genetic Function Algorithm (GFA) was identified as the most efficient and economical model for predicting 305-day fat-corrected milk yield in Nigerian herds (FCM305d = 1036.1 - 98.3RP + 22FY + 15.92UC - 0.07RUH; Adj R² = 0.997; RMSE = 30.07; BIC = 1997.28). Neural Network models demonstrated the highest prediction accuracy across environments, with the optimal architecture for predicting 305-day fat-corrected milk yield being a 6-2-1 multilayer perceptron using backpropagation with an 88% learning rate and 2% bias. Holstein Friesian cows showed the highest dairy merit for milk production in Plateau and Adamawa States, while Jersey cows exhibited optimal milk yield in Kwara State. These findings highlight substantial genetic variation for milk production, conformation, fertility, and lactation traits among multi-genotype cows across different environments.