Ixa López

The use of omega fatty acids during pregnancy and lactation could be a beneficial strategy to improve health and production in ruminants. In this study, the effect of the dietary addition of c9, t11 and t10, c12 isomers of conjugated linoleic acid (CLA) in pure soybean oil (T1) was compared with pure soybean oil (T2) and soybean oil discarded after frying fish (Centropomus undecimalis, Pagrus pagrus, and Caranx crysos; T3) on the fatty acid profile in ewe milk and the tissues of their lambs. Forty-five multiparous ewes (East Friesian × Katahdin), 32 to 35 months old, 70 days pregnant, were randomly assigned to one of the three experimental diets. The fatty acid profile, total solids, fat, protein, and lactose in milk were determined 14 days postpartum. Fifteen lambs from each treatment were sacrificed at 70 days of age, and the fatty acid profiles in perirenal fat, muscle, and brain were determined. In milk, fat and total solids content were lower with T1. On the other hand, linoleic acid content, the sum of omega-6 and DHA were higher in T1 compared to other groups (p < 0.05). In the brain, stearic acid and linoleic acid concentration increased with T1; eicosapentaenoic acid decreased and omega-3s increased with T3, while DHA was lower in T2 (p < 0.05). The addition of CLA isomers or discarded soybean oil in ewe diet from pregnancy to lactation is advantageous for the ewe since less energy in the form of milk fat is lost. In addition, increasing the concentration of omega fatty acids in ewe diet to improve omega fatty acids in lamb’s brain could be a feed strategy to increase their productivity and health.

In ecology, if the considered area or space is large, the spatial distribution of individuals of a given plant species is never homogeneous; plants form different patches. The homogeneity change in space or in time (in particular, the related change-point problem) is an important research subject in mathematical statistics. In the paper, for a given data system along a straight line, two areas are considered, where the data of each area come from different discrete distributions, with unknown parameters. In the paper a method is presented for the estimation of the distribution change-point between both areas and an estimate is given for the distributions separated by the obtained change-point. The solution of this problem will be based on the maximum likelihood method. Furthermore, based on an adaptation of the well-known bootstrap resampling, a method for the estimation of the so-called change-interval is also given. The latter approach is very general, since it not only applies in the case of the maximum-likelihood estimation of the change-point, but it can be also used starting from any other change-point estimation known in the ecological literature. The proposed model is validated against typical ecological situations, providing at the same time a verification of the applied algorithms.