Genetic response to selection for rate and efficiency of lean gain in beef cattle

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Enhancing individual animal resilience to environmental disturbances to address low productivity in dairy cattle performing in sub-Saharan Africa

The current review examines potential solutions to enhance the sustainability and productivity of the dairy sector in sub-Saharan Africa (SSA) with an emphasis on breeding for resilience. Additionally, the paper explores various indicators for measuring resilience and provides insights into the data that can be utilized to quantify resilience in SSA’s dairy production systems. Dairy production contributes significantly to food and nutritional security and employment in SSA. However, besides the general lack of enabling policy and institutional environments, production is negatively affected by environmental challenges such as high temperatures and heat stress, diseases and parasites, unreliable rainfall patterns, shortages of feeds and forages and undue preference for taurine cattle breeds regardless of their poor adaptability to prevailing local conditions. Fostering the resilience capacity of dairy animals is imperative to combat climate-related adversities and maintain productivity. This can only be achieved if reliable and practical methods for quantifying and analyzing resilience in SSA are described and undertaken. This study has reviewed variance of deviations, root mean square of deviations, autocorrelation of deviations, skewness of deviations, slope of the reaction norm and its absolute value as possible indicators of resilience in SSA. While previous research has reported genetic variation and favorable correlations of these indicators with health, fitness, and fertility traits, their potential in SSA environments requires further investigation. Besides, labor- and cost-effective phenotypic data collection is essential for characterization of resilience using these indicators. Through this study, we propose frequently collected data on milk production traits, body fat-related traits, and activity patterns as suitable in the sub-Saharan Africa context. The African Asian Dairy Genetic Gains Project by the International Livestock Research Institute (ILRI) offers a valuable opportunity to collate data from diverse dairy systems in SSA for testing the potential of these indicators. Insights from this study are helpful in improving resilience of dairy animals in SSA, which would contribute to poverty alleviation, animal welfare improvement, and better preparedness in lieu of climate change in SSA.</p

Genomic evaluations using data recorded on smallholder dairy farms in low- to middle-income countries

Breeding has increased genetic gain for dairy cattle in advanced economies but has had limited success in improving dairy cattle in low- to middle-income countries (LMIC). Genetic evaluations are a central component of delivering genetic gain, because they separate the genetic and environmental effects of animals' phenotypes. Genetic evaluations have been successful in advanced economies because of large data sets and strong genetic connectedness, provided by the widespread use of artificial insemination (AI) and accurate recording of pedigree information. In smallholder dairy production systems of many LMICs, the limited use of AI and small herd sizes results in a data structure with insufficient genetic connectedness between herds to facilitate genetic evaluations based on pedigree. Genomic information keeps track of shared haplotypes rather than shared relatives captured by pedigree records. Therefore, genomic information could capture “hidden” genetic relationships, that are not captured by pedigree information, to strengthen genetic connectedness in LMIC smallholder dairy data sets. This study's objective was to use simulation to quantify the power of genomic information to enable genetic evaluation using LMIC smallholder dairy data sets. The results from this study show that (1) genetic evaluations using genomic information were more accurate than those using pedigree information in populations with a high effective population size and weak genetic connectedness; and (2) genetic evaluations modeling herd as a random effect had higher or equal accuracy than those modeling herd as a fixed effect. This demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in situ records collected from herds of ≤4 cows. The establishment of routine genomic evaluations could allow the development of LMIC breeding programs comprising an informal set of nucleus animals distributed across many small herds within the target environment. These nucleus animals could be used for genetic evaluation, and the best animals could be disseminated to participating smallholder dairy farms. Together, this could increase the productivity, profitability, and sustainability of LMIC smallholder dairy production systems