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Past developments in livestock breeding have led to considerable genetic change in production traits, but the follow-up of nutrition and management is often incomplete. The pig production sector is moving to hotter climates, and to more intensive and limiting conditions. This increases demands for animal robustness. Robustness can be implemented as a breeding objective trait just like production traits. Breeding for robustness is feasible, but requires substantial investment in data and technology. As for all low-heritability traits with complicated data recording, DNA markers provide a useful tool to support selection; this requires good association studies and ongoing multiple marker development. Breeding for increased robustness must be implemented in balance with breeding for increased production. It is therefore useful to define robustness in terms of performance-relevant issues. A convenient approach is through the environmental sensitivity of the expression of genetic production potential. Environmental sensitivity illustrates loss of flexibility to deal with intensive or limiting conditions, due to unbalanced resource allocation. It can be quantified for individual animals in terms of reaction norm parameters, which can be used as estimated breeding values to support selection. The challenges of implementing such a system will be (i) the set-up of proper data collection in a wide range of environmental settings; (ii) the development of proper data processing tools; (iii) the design of suitable breeding objectives and selection criteria, including MAS; and (iv) the successful integration of the first 3 objectives.

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A Bayesian procedure was used to estimate linear reaction norms (i.e. individual G 3 E plots) on 297 518 litter size records of 121 104 sows, daughters of 2040 sires, recorded on 144 farms in North and Latin America, Europe, Asia and Australia. The method allowed for simultaneous estimation of all parameters involved. The analysis was carried out on three subsets, comprising (i) parity 1 records of 33 641 sows of line B, (ii) all parity records of 52 120 sows of line B and (iii) all parity records of 121 104 sows of lines A, B and A 3 B. Estimated heritabilities ranged from 0.09 to 0.10 (smallest to largest subset) for the intercept of the reaction norms, and were 0.15, 0.08 and 0.02 (ditto) for the slope. Estimated genetic correlations between intercept and slope were 20.09, 10.26 and 10.69 (ditto). The three subsets therefore showed a progressively lower genetic component to environmental sensitivity, and progressively less re-ranking of genotypes across the environmental (herd-year-season) range. In a genetic evaluation that does not include reaction norms in the statistical model, part of the G 3 E effect remains confounded with the additive genetic effect, which may lead to errors in the estimates of the additive genetic effect; the reaction norms model removes this confounding. The intercept estimates from the largest data subset show correlations with litter size estimated breeding values (EBV) from routine genetic evaluation (without reaction norms included) of 0.78 to 0.85 for sows with one to seven litter records, and 0.75 for sires. Hence, including reaction norms in genetic evaluation would increase the reliability of the EBV of young selection candidates without own performance or progeny data by considerably more than 100 3 (1/0.7521) 5 33%. Reaction norm slope estimates turn out to be very demanding statistics; environmental sensitivity must therefore be classified as a 'hard-to-measure' trait.

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