آنالیز ژنتیکی چند صفته پارامترهای منحنی شیردهی گاومیش های خوزستان

Introduction: Improving the performance of economic traits of domestic livestock by means of sounds breeding programs could elaborate meeting the protein demands of the human world population. The water buffalo, also called the domestic water, bears up substantial value in terms of milk and meat production. Khuzestani buffalo, the subspecies of domestic Asian water buffalo (Bubalus bubalis) has become a strategic animal species in Khuzestan province, Iran, due to its ability to adapt to adverse environmental conditions, low quality feed sources dependency and high quality products. Archaeological and ancient records show little evidence regarding the expansion of the Khuzestani buffalo. Many people in the region depend on this species for their livelihoods than on any other domestic animal. In this regards, the buffalo lactation could have crucial role in farmers' economy. In general, having knowledge of lactation curve, may shed some lights on management and farm-based operation of milk production system. The lactation curve is a graphical representation tool that reflects the effect of couple of numbers of different factors on milk production during lactation; therefore, it is useful in making better management decisions. In other words, it could summarize the pattern of milk production, biological and economic efficiency of the animal. Several models have been suggested to describe the lactation curves. In this study, in order to estimate the association between different fitted lactation curves and their comparisons on Khuzestani buffalo, six nonlinear mathematical models (Wood, Wilmink, inverse polynomial, Complex logarithm, Ali and Schaefer and Digestra) were used. Material and methods: The data used in this study, consisted of 103760 test day records of milk yield of 14280 buffalu in the first parity that collected by the Agricultural Jihad Center of Khuzestan province during 1993 to 2020. The Microsoft Excel was used to edit the data. Animals with unknown birth and calving dates were removed. Those fixed effects that had significant effects on the coefficients of lactation curve (P < 5%) were included in the final model. The lactation curve parameters were estimated using the nonlinear regression procedure (NLIN) of SAS software version 9.4 on test day records. Evaluation of goodness of fit was based on the coefficient of determination (R2), root mean square error (RMSE) and Akaike information criterion (AIC). The (Co) variance components were estimated by multi-trait animal model using restricted maximum likelihood method run in WOMBAT software. Results and discussion: Descriptive statistics of milk production of Khuzestani buffaloes based on 103760 records from different areas shown that the average milk production was 8.048 kg, standard deviation was 3.047 and coefficient of variation was 37.87 as well. Wood's mathematical model provided the best fit of the lactation curve due to the highest value for the R2 and lower values for RMSE and AIC compared to the other models and it obtained as 0.88. The a, b and c parameter values of Wood’s model were estimated 6.91, 0.057 and 0.00083, respectively. The results showed that the wood model was able to estimate the time of peak milk yield more accurately than the other models. The peak milk yield of Khuzestani buffaloes (8.30 kg/d) appeared on average in the tenth week (day 69) after calving. The heritability of lactation curve traits for initial yield (a), upward slope of the curve (b) and downward slope of the curve (c) were obtained 0.57, 0.27 and 0.48, respectively. In this study, the persistency(s) value for typical lactations were lactation curve were estimated 7.49. The genetic correlations among lactation curve parameters ranged from -0.55 (upward slope of the curve and initial yield) to 0.138 (upward and downward slope of the curve), whereas the phenotypic correlations ranged from -0.35 (Between initial yield and the upward slope of the curve) to +0.196 (b and c). The solutions of mixed model equations revealed that Buffalo with IDs 3436, 3410, 3360, 2868, 24, and 3174 had the highest breeding value for coefficient a. In other hand, buffalo 3438, 3409, 3418, 3361, 3282, and 3169 showed the highest breeding value in the coefficient b. Finally, the buffaloes that obtained the highest hereditary value in different coefficients of lactation curve were introduced as the superior buffaloes. By knowing the best buffaloes in terms of breeding value for parameters a and b, it is possible to select buffaloes to be parent for next generation. In general, evaluating lactation curve equations using nonlinear models and multi-trait analysis is an effective step in identifying buffaloes with high genetic potential to improve and increase milk production efficiency. The results of the present study showed that among the six mathematical models studied, Wood's incomplete gamma function had more viability for fitting the lactation curve of Khuzestani buffalo. The effect of calving season was not significant on lactation parameters while year of calving had a significant effect. Negative and moderate genetic correlations between initial production parameters (a) and upward slope (b) of the lactation curve indicate that keeping on with higher initial production level will have a lower rate of increase until peak production. In the present study, multivariate analysis led to the relationship of all coefficients of the lactation curve with each other and as a result more realistic parameters were estimated for them. It is suggested that in future studies, a computer-aided economic production system be designed and the economic coefficients related to the parameters of the lactation curve be estimated for integration into the selection index. Also, in order to improve the performance of Khuzestani buffalo, it is recommended to study the genomic relationship of milk production curve parameters.