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A study was conducted to determine the bioavailabilities of selenium (Se) from Se-enriched whole milk in artificially reared neonatal pigs. Results indicated that Se in milk from cows fed selenised yeast is highly bioavailable and may offer a way of delivering supplemental Se to humans too.

• The value of extra pasture DM for livestock production can be estimated using several different methods. • The two-price market value method can be an efficient way to estimate the value of extra seasonal dry matter yield of pasture grown and used. • To demonstrate its application, the method is used to value the seasonal yield of 19 perennial ryegrass cultivars grown in temperate Australia. • The value of extra pasture estimated using the two-price market value method can be easily generalised across a range of farming systems. The challenges of estimating the value of growing and using extra pasture to the farm business often necessitates using complex systems modelling of individual farms. In this paper, a straightforward market value method is outlined, where technical substitutes for which there is a defined market price are used as proxies to estimate an equivalent value for extra pasture used in a farm system. The method can be useful for all grazed livestock production systems where substitutes for pasture are purchased and sold. The two-price approach presented advances on previous applications of using market values by accounting for differences in the value of extra pasture DM used on farm in different seasons. The value per extra kg DM harvested in seasons when pasture typically limits livestock production, for example during summer/autumn in temperate environments, and used on farm equates to the market price of the replacement cost as purchased supplementary feed. This is the maximum the extra pasture DM could be worth when used in the farm system. Conversely, the value per extra kg DM harvested in spring when extra pasture is likely to be in surplus of livestock requirements, equates to its market price if sold, as standing hay for example, termed the salvage value. This two-price approach was applied to value the previously published seasonal DM yield differences of 19 perennial ryegrass cultivars, compared to an industry standard cultivar, grown in three regions of Victoria, Australia. Results were then compared to the value of extra pasture DM previously estimated using system simulation analyses of three farms and applied to the same perennial ryegrass data set. The difference in the value of extra pasture DM estimated using the two methods ranged from $0.01/kg DM to $0.24/kg DM. Overall, the two-price method estimated an extra $24 to $200/ha.year benefit over the industry standard cultivar for the 19 cultivars across the three regions, similar to the extra $0 to ∼ $180/ha.year estimated by the previous system simulation analysis. The two-price market value method can be an efficient way to estimate the value of extra seasonal DM yield of pasture, with results easily generalised across a range of farming systems. [ABSTRACT FROM AUTHOR]

• Accurate seasonal predictions of ryegrass cultivar DM yields using LMMs. • Base AR 37 and Bealy NEA2 were the best performing cultivars based on annual ranking. • Seasonal variability was found to be larger than genotypic variability. • An antedependence of order 3 was the best residual covariance structure for harvests. With over 60 commercial perennial ryegrass cultivars available on the market in Australia, selecting cultivars with high dry matter (DM) yield and economic profitability requires accurate estimates of their DM yield across target environments. This study, using data from multi-environment multi-harvest (MEMH) trials conducted in south-eastern Australia, derived accurate seasonal predictions of DM yield of these cultivars using linear mixed models (LMM). Base AR37 and Bealy NEA2 were found to be the best performing cultivars in most of the seasons in the target south-eastern Australian environments. Seasonal variability was found to be larger than genotypic variability as usually is the case in multi-environment trials. We have provided details of the LMM methodology used, along with ASReml-R code, to enable others to apply it in similar studies with appropriate changes as required for dataset used. The statistical theory underlying this methodology has also been briefly described in an Appendix for interested readers. [ABSTRACT FROM AUTHOR]