Regression tree analysis for predicting body weight of Nigerian Muscovy duck (Cairina moschata)

Abstract

Morphometric parameters and their indices are central to the understanding of the type and function of livestock. The present study was conducted to predict body weight (BWT) of adult Nigerian Muscovy ducks from nine (9) morphometric parameters and seven (7) body indices and also to identify the most important predictor of BWT among them using regression tree analysis (RTA). The experimental birds comprised of 1,020 adult male and female Nigerian Muscovy ducks randomly sampled in Rain Forest (203), Guinea Savanna (298) and Derived Savanna (519) agro-ecological zones. Result of RTA revealed that compactness; body girth and massiveness were the most important independent variables in predicting BWT and were used in constructing RT. The combined effect of the three predictors was very high and explained 91.00% of the observed variation of the target variable (BWT). The optimal regression tree suggested that Muscovy ducks with compactness >5.765 would be fleshy and have highest BWT. The result of the present study could be exploited by animal breeders and breeding companies in selection and improvement of BWT of Muscovy ducks.

Modelling Growth Of Dual-purpose Sasso Hens In The Tropics Using Different Algorithms

“…Therefore, it has been reported in many cases that growth models with a flexible inflexion point as observed in the present study, fit growth data better [25]. In their own submissions, asymptotic or adult weight of an animal, the rate at which the adult weight is attained [16] and the standardized age at the inflection point of the growth curve [26] are parameters of importance that can be exploited by geneticist and breeders alike to increase productivity. The maturing rate (k) values of 0.096 and 0.105 of the current study appear to be close to the 0.137 reported for an Italian nondescript chicken breed from 2-24 weeks of age [7].…”

“…Week of rearing and housing systems were fitted as the independent variables. The CRT reveals graphically the most important attributes that permits the prediction of the dependent variable [16]. The efficiency of the CRT model was determined using coefficient of determination (R 2 ) as well as the risk value and its associated standard error.…”

“…In a related study, Oguntunji [16] estimated BW from some biometric parameters of Muscovy ducks using CRT, with high prediction accuracy.…”

“…ANN is a more recent robust technique that has been used to model growth in livestock [9][10][11][12], forecast the number and weight of eggs [13] and fit egg production curves [14]. Classification and regression tree (CRT) is another classical statistical approach to model growth as it is not affected by multicollinearity problem [15,16]. Sasso hens are newly introduced into the Nigerian tropical environment.…”

Analysis by Data Mining Methods of Relationships among Egg Quality Characteristics in Japanese Quails

“…Ayrıca, sarı indeksi ve Haugh birimini yaş, ak indeksini ise yaş ve yumurta boyu etkilemiştir. Oguntunji (2017), Muscovy ördeklerinin vücut ağırlığının tahmininde CART algoritmasını kullanmış olup, R 2 değerini 0.910 olarak bildirmiştir.…”