Random regression test-day parameters for first lactation milk yield in selection and production environments in Kenya

Evaluation of crossbreeding strategies for improved adaptation and productivity in African smallholder cattle farms

BACKGROUND

Crossbreeding is successfully implemented worldwide to improve animal productivity and adaptability. However, recurrent failures of crossbreeding programmes in African countries imply the need to design effective strategies for the predominant smallholder production systems.

METHODS

A comprehensive simulation procedure mimicked body weight (BWL) and tick count (TCL) incidence in a local taurine cattle breed and in an exotic indicine beef cattle breed (BWE and TCE, respectively). The two breeds were crossed to produce F1 and rotational animals. Additionally, synthetic breeds were created by applying four schemes defined as farm bull (FB), intra-village bull (IVB), exchanged-village bull (EVB), and population-wide bull (PWB) scheme. These schemes reflect different strategies to select and allocate bulls to smallholder farms. The different crosses were compared with the local breed over 20 generations by varying the genetic correlation between the traits ( r g = - 0.4, 0, 0.4), genotype-by-environment effects (GxE) between local and exotic environment ( r g × e = 0.4, 0.6, 0.8), and the relative emphasis of TCL compared to BWL in a selection index (SI_TCL10%, SI_TCL30%, SI_TCL50%).

RESULTS

Regardless of r g and r g × e , EVB achieved the highest phenotypic and genetic gains for BWL and TCL over the 20 generations with SI_TCL50%. However, EVB displayed lower phenotypic means than F1 crosses in the first seven generations due to the loss of heterosis. Additive genetic variances were generally larger in synthetic crosses than in F1 and local animals, explaining the larger responses to selection. In addition, the EVB was the most effective strategy to stabilize inbreeding and retain heterosis in the advanced generations of synthetic animals. Low emphasis on TCL (SI_TCL30%, SI_TCL10%) resulted in negative phenotypic gain for TCL in synthetic animals when rg = - 0.4. In contrast to F1 and rotational crosses, GxE effects did not affect phenotypic gain in synthetic crosses.

CONCLUSIONS

The study demonstrates opportunities for long-term genetic improvement of adaptive and productive performances in smallholder cattle farms using synthetic breeding. Extensive exchange of semen between villages or regions controls inbreeding and additionally contributes to increasing genetic gain. Furthermore, the definition of a suitable selection index prevents antagonistic selection responses caused by negative correlations between traits and GxE effects.

Modeling heat stress effects on dairy cattle milk production in a tropical environment using test-day records and random regression models

In tropical environments, dairy cattle production is constrained by several factors, including climate. The seasonal loss of milk due to heat stress is a recurring challenge for many dairy producers. The objective of this study was to detect heat stress thresholds, milk yield loss and individual animal variations using random regression models for dairy cattle from test-day milk records. Data were obtained from the Kenya Livestock Breeders Organization for the years 2000-2017 and merged with weather data. The weather parameters were grid-interpolated solar and meteorological data obtained from the National Aeronautics and Space Administration/Prediction Of Worldwide Energy Resources (NASA/POWER). After editing, the records comprised 49 993, 45 251 and 36 136 test-day records for first, second, and third lactations, respectively, for the four main dairy breeds: Friesian (68.0%), Ayrshire (21.1%), Jersey (7.6%) and Guernsey (3.3%). Variance components were estimated using Restricted Maximum Likelihood in ASReml software. Random regression models with third-order Legendre polynomials were fitted to the average and individual lactation curves and the reaction norms. An extended factor analytic variance structure for the random cow effects was used to estimate (co)variances between days in milk and thermal load. The daily average temperature (TA) and temperature humidity index (THI) were identified as the most suitable thermal load indicators for assessing milk yield losses. Considering a one day lag, the estimated heat stress thresholds were about 22 °C and 69 index units for TA and THI, respectively. Almost no differences were observed for estimated residual variances between the thermal load indicators, indicating there was no better model fit by TA or THI. The heat stress thresholds and milk loss patterns are important for management of dairy production systems in the tropics with climatic conditions similar to this study. Data recording should be improved as a tool to monitor the expected impacts of climate change and mitigation measures.

Genetic parameters for test-day milk yield, lactation persistency, and fertility in low-, medium-, and high-production systems in Kenya

Genetic parameters for test-day milk yield, lactation persistency, and age at first calving (as a fertility trait) were estimated for the first 4 lactations in multiple-breed dairy cows in low-, medium-, and high-production systems in Kenya. Data included 223,285 test-day milk yield records from 11,450 cows calving from 1990 to 2015 in 148 herds. A multivariate random regression model was used to estimate variance and covariance components. The fixed effects in the model included herd, year, and test month, and age as a covariate. The lactation profile over days in milk (DIM) was fitted as a cubic smoothing spline. Random effects included herd, year, and test month interaction effects, genetic group effects, and additive genetic and permanent environmental effects modeled with a cubic Legendre polynomial function. The residual variance was heterogeneous with 11 classes. Consequently, the variance components were varied over the lactation and with the production system. The estimated heritability for milk yield was lower in the low-production system (0.04-0.48) than in the medium- (0.22-0.59) and high-production (0.21-0 60) systems. The genetic correlations estimated between different DIM within lactations decreased as the time interval increased, becoming negative between the ends of the lactations in the low- and medium-production systems. Low (0.05) to medium (0.60) genetic correlations were estimated among first lactation test-day milk yields across the 3 production systems. Genetic correlations between the first lactation test-day milk yield and age at first calving ranged from 0.27 to 0.49, 0 to 0.81, and -0.08 to 0.27 in the low-, medium-, and high-production systems, respectively. Medium to high heritabilities (0.17-0.44) were estimated for persistency, with moderate to high (0.30-0.87) genetic correlations between 305-d milk yield and persistency. This indicates that genetic improvement in persistency would lead to increased milk yield. The low to medium genetic correlations between test-day milk yield between production systems indicate that sires may be re-ranked between production systems. Therefore, we conclude that sires should be selected based on a genetic evaluation within the target production system.

Genetic evaluation of test-day milk yields from smallholder dairy production systems in Kenya using genomic relationships

Efforts to improve dairy production in smallholder farming systems of East Africa over the past decade have had limited impact because of the lack of records on performance to guide targeted breeding programs. Estimates of genetic parameters in these systems are lacking. Using data generated through a project (“Germplasm for Dairy Development in East Africa”) in Kenya and a genomic relationship matrix from genotypic records, we examined the potential impact of different models handling contemporary groups or herd effects on estimates of genetic parameters using a fixed regression model (FRM) for test-day (TD) milk yields, and the covariance structure for TD milk yield at various stages of lactation for animals using a random regression model (RRM). Models in which herd groups were defined using production levels derived from the data fitted the data better than those in which herds were grouped depending on management practices or were random. Lactation curves obtained for animals under different production categories did not display the typical peak yield characteristic of improved dairy systems in developed countries. Heritability estimates for TD milk yields using the FRM varied greatly with the definition of contemporary herd groups, ranging from 0.05 ± 0.03 to 0.27 ± 0.05 (mean ± standard error). The analysis using the RRM fitted the data better than the FRM. The heritability estimates for specific TD yields obtained by the RRM were higher than those obtained by the FRM. Genetic correlations between TD yields were high and positive for measures within short consecutive intervals but decreased as the intervals between TD increased beyond 60 d and became negative with intervals of more than 5 mo. The magnitude of the genetic correlation estimates among TD records indicates that using TD milk records beyond a 60-d interval as repeated measures of the same trait for genetic evaluation of animals on smallholder farms would not be optimal. Although each individual smallholder farmer retains only a few animals, using the genomic relationship between animals to link the large number of farmers operating under specified environments provides a sufficiently large herd-group for which a breeding program could be developed.