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Smallholder dairy cattle production systems in Africa are intensifying production through importation of germplasm from breeding programs conducted in temperate regions to improve commercial cow populations. Presence of genotype by environment interaction results in unfavorable correlated responses. The aim this thesis was to develop strategies for breeding programs in developing countries that can support sustainable intensification of these systems. Specific objectives were (a) to determine desired gains for breeding objective traits, (b) compare progeny testing (PT) and genomic selection (GS) selection strategies, (c) evaluate the economic performance of PT and GS selection strategies and (d) compare genetic gains for economic and non-economic breeding objectives; the Kenya dairy cattle sector was used as a working example. To account for the limited pedigree and performance recording, a five-trait breeding objective and small-sized breeding program were studied. Breeding objective traits, determined based on producer preferences, were milk yield (MY), production lifetime (PLT), calving interval (CI), fat yield (FY) and mature body weight (MBW). Producers were categorized into high intensive group, who placed highest preference on PLT and MY, and low intensity group that placed highest preferences on CI and PLT. MY and FY were the most important traits for processors. Consensus desired gains, based on weighted goal programming, were 2.51, 2.42, 0.22, 0.87 and 0.15% for PLT, MY, CI, FY and MBW, respectively. Comparison of breeding schemes shows that GS schemes had lower accuracies but gave higher responses per year due to shorter generation intervals. Besides genetic gains, economic performance underpins the adoption of selection strategies. GS schemes had between 3.2 and 5.2-fold higher cumulated genetic gain in the commercial cow population and higher gross margins compared to PT schemes. Semen storage made PT schemes more profitable but less so than GS schemes. Functional traits can increase the sustainability of resource poor smallholder systems under harsh environments. Economic breeding objectives yielded undesirable responses in functional traits. Breeding objectives based on desired gains or non-market objectives improved response in functional traits but at a monetary cost. It is concluded that sustainable productivity of smallholder systems can be improved by implementation of local breeding program based on GS, but this requires more emphasis on functional traits, which can be achieved by use of non-economic objectives.

Improved reproductive performance has a substantial benefit for the overall profitability of dairy cattle farming by decreasing insemination and veterinary treatment costs, shortening calving intervals, and lowering the rate of involuntary culling. Unfortunately, the low heritability of classical fertility traits derived from calving and insemination data makes genetic improvement by traditional animal breeding slow. Therefore, there is an interest in finding novel measures of fertility that have a higher heritability or using genomic information to aid genetic selection for fertility. The overall objective of this thesis was to explore the use of milk progesterone (P4) records and genomic information to improve selection for fertility in dairy cows. In a first step, the use of in-line milk progesterone records to define endocrine fertility traits was investigated, and genetic parameters estimated. Several defined endocrine fertility traits were heritable, and showed a reasonable repeatability. Also, the genetic correlation of milk production traits with endocrine fertility traits were considerably lower than the correlations of milk production with classical fertility traits. In the next step 17 quantitative trait loci (QTL) associated with endocrine fertility traits, were identified on Bos taurus autosomes (BTA) 2, 3, 8, 12, 15, 17, 23, and 25 in a genome-wide association study with single nucleotide polymorphisms. Further, fine-mapping of target regions on BTA 2 and 3, identified several associated variants and potential candidate genes underlying endocrine fertility traits. Subsequently, the optimal use of endocrine fertility traits in genomic evaluations was investigated; using empirical and theoretical predictions for single-trait models, I showed that endocrine fertility traits have more predictive ability than classical fertility traits. The accuracy of genomic prediction was also substantially improved when endocrine and classical fertility traits were combined in multi-trait genomic prediction. Finally, using deterministic predictions, the potential accuracy of multi-trait genomic selection when combining a cow training population measured for the endocrine trait commencement of luteal activity (C-LA), with a training population of bulls with daughter observations for a classical fertility trait was investigated. Results showed that for prediction of fertility, there is no benefit of investing in a cow training population when the breeding goal is based on classical fertility traits. However, when considering a more biological breeding goal for fertility like C-LA, accuracy is substantially improved when endocrine traits are available from a limited number of farms.