Investigation of egg production curve in ostrich using nonlinear functions

In most countries, ostrich farming is considered a developing branch of the efficient poultry industry. The profitability of ostrich farm requires specific consideration of productions features such as the female fertility, egg production, hatchability, and growth performance. Hence, this study aimed to fit nonlinear functions to describe the ostrich egg production pattern to achieve the most appropriate and recommendable mathematical function for future studies. For this purpose, 14,507 daily records of 184 female ostriches in 5 production seasons (periods) during 2016 to 2021 were used. Five nonlinear functions including Incomplete gamma (Wood function), Corrected gamma (McNally), nonlinear Logistic (Yang), Logistic (Nelder), and Lokhorst were fitted for modeling the egg production curve in ostrich. The goodness of fit criteria's including Mean Square Error (MSE), Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and selection of the best function. The results indicated that the Wood and the McNally functions with a slight difference in all fitting criteria were the best-fitted functions and the Yang function with the highest values of MSE, LRT, AIC, BIC, were the most inappropriate function to describe the ostrich egg production curve. The McNally and the Wood can be recommended as appropriate functions to describe egg production during 5 production seasons in the studied ostrich flock.

Analysis of lactation feed intakes for sows with extended lactation lengths

The objectives of this research were to quantify and model daily feed intakes to 28 d of lactation in modern sows. A total of 4,512 daily feed intake (DFI) records were collected for 156 Hypor sows from February 2015 to March 2016. The mean lactation length was 27.9 ± 2.0 d. The data included 9 parity 1, 33 parity 2 and 114 parity 3+ sows. Data were collected using a computerized feeding system (Gestal Solo, JYGA Technologies, Quebec, Canada). The feeding system was used to set an upper limit to DFI for the first 7 d of lactation. Overall, the least-squares means of a model including the random effect of sow indicated that DFI's continued to slowly increase to 28 d of lactation. The DFI data were fitted to Generalized Michaelis-Menten (GMM) and polynomial functions of day of lactation (t). The GMM function [DFI_i,t (kg/d) = DFI₀ + (DFI_A - DFI₀)(t/K)^C/[1 + (t/K)^C]] was fitted with 2 random effects for DFI (dfi_Ai) and intercept (dfi_0i) using the NLMIXED procedure in SAS®. The polynomial function DFI_i,t (kg/d) = [B₀ + B₁ t + B₂ t² + B₃ t³ + B₄ t⁴] was fitted with three random effects for B₀, B₁, and B₂ using the MIXED procedure in SAS®. Fixed effects models of the two functions had similar Akaike's Information Criteria (AIC) values and mean predicted DFI's. The polynomial function with 3 random effects provided a better fit to the data based on R² 30 (0.81 versus 0.79), AIC (14,709 versus 15,158) and RSD (1.204 versus 1.321) values than the GMM function with two random effects. The random effect for B₂ in the polynomial function allowed for the fitting of the function to lactation records that had decreased DFI after 15 d of lactation. The random effects for the polynomial function were used to sort the lactation records into three groups based on the derivative of the function at 21 d of lactation. Lactation records of the three groups had similar DFI the first two weeks of lactation (P > 0.40). The three groups of sows had substantially different DFI's after 18 d of lactation (P < 0.028). The differences in both actual and predicted DFI's between the three groups increased with each day of lactation to day 28 (P < 0.001). Mixed model polynomial functions can be used to identify sows with different patterns of DFI after 15 d of lactation.