Breeding for resistance against nematodes has become the need of the hour due to emergence of anthelmintic resistant strains of major pathogenic nematodes of economic importance and rising demand for chemical residue free food by consumers. ICAR-Central Sheep and Wool Research Institute Avikanagar (Rajasthan) has developed Haemonchus contortus resistant lines of sheep in Avikalin and Malpura breeds by harvesting benefits of over-dispersion in fecal egg counts (FEC) through executing a breeding program since year 2004. Aim of the present study was to assess the genetic parameters for nematode resistance in these lines and also to develop suitable criteria for selection targeting resistance as well as growth improvement in these two lines. The data on 1240 Avikalin and 2172 Malpura sheep generated over 13 years (2004–16) for FEC along with deep pedigree and growth records for live weight at 6 (6WT) and 12 month were used for study. Data were analyzed using Average Information Restricted Maximum Likelihood (AIREML) approach. Results revealed moderate heritability (h2) for pre-drench log transformed fecal egg count (LFEC) in Avikalin (0.21±0.06) and Malpura (0.18±0.04) sheep. The post-drench h2 for LFEC was low in Avikalin (0.04±0.03) and Malpura (0.11±0.03) sheep. Effective selection program can be carried out for further improving the resistance against H. contortus in both the breeds using pre-drench LFEC estimates. The genetic correlation between the pre-drench LFEC and growth traits was not in the desired direction. Existence of substantial genotype × environment (G×E) interaction was seen in Malpura sheep, where major shift in ranks of sheep based on pre-drench LFEC as that of post-drench LFEC was observed owing to genetic correlation of 0.65±0.15. The G×E was absent in Avikalin sheep. Unreliable genetic correlation between growth and LFEC does not warrant a multi trait selection index development and its utilization in breeding program. The independent selection for LFEC followed by corrected 6WT can precisely help in achieving the goal of improving growth in nematode resistant sheep.

In this study, the economic values for production and functional traits of dairy sheep are estimated through the application of a profit function model using farm-level technical and economic data. The traits incorporated in the model were milk production, prolificacy, fertility, milking speed, longevity and mastitis occurrence. The economic values for these traits were derived as the approximate partial derivative of the specified profit function. A sensitivity analysis was also conducted in order to examine how potential changes in input and output prices would affect the breeding goal. The estimated economic values of the traits revealed their economic impact on the definition of the breeding goal for the specified production system. Milk production and fertility had the highest economic values (€40.30 and €20.28 per standard genetic deviation (SDa)), while, mastitis only had a low negative value of −0.57 €/SDa. Therefore, breeding for clinical mastitis will have a minor impact on farm profitability because it affects a small proportion of the flock and has low additive variance. The production traits, which include milk production, prolificacy and milking speed, contributed most to the breeding goal (70.0%), but functional traits still had a considerable share (30.0%). The results of this study highlight the importance of the knowledge of economic values of traits in the design of a breeding program. It is also suggested that the production and functional traits under consideration can be categorized as those which can be efficiently treated through genetic improvement (e.g. milk production and fertility) while others would be better dealt with through managerial interventions (e.g. mastitis occurrence). Also, sub-clinical mastitis that affects a higher proportion of flocks could have a higher contribution to breeding goals.

In a stochastic simulation study the effect of simultaneously changing the model for prediction of breeding values and changing the breeding goal was studied. A population of 100 000 cows with registrations on seven traits was simulated in two steps. In the first step of 15 years the population was selected for production and mastitis occurrence using a univariate model for prediction of breeding values for production and a trivariate model using information on mastitis treatments, udder depth and somatic cell score for prediction of breeding values for mastitis occurrence. In the second step six different scenarios were set up and simulated for 15 years combining two different breeding goals and three different models for prediction of breeding values in 20 replicates. Breeding goal 1 had relative economic value per genetic standard deviation on production (19.4) and mastitis occurrence ( − 50) whereas breeding goal 2 had a economic value on production (19.4), udder depth (4.2), mastitis occurrence ( − 50), non return rate (13.0) and days open ( − 16.75). Model 1 was a model similar to the one used in the first 15 years. Model 2 was an approximate multitrait model where solutions for fixed effects from a model corresponding to model 1 were subtracted from the phenotypes and a multitrait model with an overall mean, a year effect, an additive genetic and a residual effect were applied. Model 3 was a full multitrait model. Average genetic trends for total merit and each individual trait over 20 replicates were compared for each scenario. With the number of replicates the genetic responses using model 2 and 3 were not significant different. With a broad breeding goal using, model 2 or model 3 gave a significantly higher response in total merit than using model 1. Using a narrow breeding goal there was no significant difference between models used for prediction of breeding values. Results showed that with a breeding goal with a lot of emphasis on low heritable traits with a high economic value using a multitrait methodology for prediction of breeding values will redistribute the genetic progress in the total merit index. More gain will come from the low heritable traits in the breeding goal and less from traits with higher heritability. With a broad breeding goal and exploiting the available information in the data the inbreeding coefficient increased though not significantly.