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- 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.
- ItemRESPONSE OF GROUNDNUT (Arachis hypogaea L.) VARIETIES TO WEED MANAGEMENT STRATEGIES AND TIME OF PHOSPHORUS FERTILIZER APPLICATION IN NORTHERN GUINEA AND SUDAN SAVANNA, NIGERIA(2022)Field trials were conducted in 2018 and 2019 wet seasons at the Teaching and Research farm of Samaru College of Agriculture, Ahmadu Bello University, Zaria Kaduna State and the Minjibir Research farm of Agricultural Research Station, Kano State of the Institute for Agricultural Research , Ahmadu Bello University, Zaria, to evaluate the response of groundnut varieties to weed management strategies and time of phosphorus fertilizer application in the northern Guinea and Sudan savanna of Nigeria. The treatments consisted of three groundnut varieties (SAMNUT-22, SAMNUT-23 and SAMNUT-24), five weed control management strategies (weedy check; black polythene mulch; pendimethalin at 1.5 kg a.i. ha-1 as pre- emergence herbicide followed by (fb) fluazifop-p-butyl at 1.0 kg a.i ha-1 applied at 3 WAS; intra-row spacing at 10 cm; hoe weeding at 3 and 6 WAS); and two different times of phosphorus (P) fertilizer application (i.e 2 weeks before sowing and at sowing). The treatments were arranged in a split plot design with factorial combinations of weed control strategies and time of P application in the main plot, and groundnut variety were allocated to the sub-plots. The treatments were replicated three times. The most predominant weed species in Samaru at 2018 and 2019 were Oldenlandia herbacea, Vernonia cinerea, Ludwigia abyssinica and Ageratum conyzoides, while in Minjibir at both years the most predominant weed species were Oldenlandia herbacea, Alternanthera sessilis, Hyptis lanceolata, Commelina diffusa subsp. diffusa. The results from the study revealed that SAMNUT-24 recorded the least weed dry weight, weed cover score and had better weed control efficiency than SAMNUT-22 and SAMNUT-23. Furthermore, SAMNUT-24 had wider canopy spread, taller plants, higher crop growth rate (CGR) and leaf area index (LAI), more nodules and better crop vigour compared with the two other varieties. The same variety flowered earlier and produced the highest pod and haulms yield. Evaluating weed management strategies; use of black polythene as mulch conferred significantly advantages with respect to weed control efficiency, low weed dry weight, weed cover score, higher CGR, LAI, canopy spread, pod yield, haulm yield, kernel yield and other yield components. Also, higher relative growth rate and nodule count were recorded under with black polythene mulch and hoe weeding at 3+ 6 WAS than in other weed control methods. Time of P application had no significant effect on weed, growth and yield characters evaluated. Groundnut pod yield was positively and highly correlated with haulm yield. In conclusion, the use of SAMNUT-24 with black polythene mulch and applying P 2 weeks before sowing or at sowing gave the highest yield of 2.50 t ha-1 at Samaru and 2.41 t ha-1 at Minjibir. Also, the use of SAMNUT-24 with black polythene mulch and applying P at sowing gave the highest net farm income at Samaru (N 1,405,643) and Minjibir (N 1,434,036) respectively. The following recommendations are drawn from the study; SAMNUT-24 is recommended for better pod and haulms yield groundnut. For effective weed control black polythene mulch or hoe weeding at 3 and 6 WAS are recommended for boosting productivity of groundnut. Application of phosphorous fertilizer at sowing is recommended for minimizing cost of production around Samaru and Minjibir.