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Net mineral requirements of dairy goats during pregnancy
- C. J. Härter, L. D. Lima, D. S. Castagnino, H. O. Silva, F. O. M. Figueiredo, N. R. St-Pierre, K. T. Resende, I. A. M. A. Teixeira
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Mineral requirements of pregnant dairy goats are still not well defined; therefore, we investigated the net Ca, P, Mg, Na and K requirements for pregnancy and for maintenance during pregnancy in two separate experiments. Experiment 1 was performed to estimate the net Ca, P, Mg, Na and K requirements in goats carrying single or twin fetuses from 50 to 140 days of pregnancy (DOP). The net mineral requirements for pregnancy were determined by measuring mineral deposition in gravid uterus and mammary gland after comparative slaughter. In total, 57 dairy goats of two breeds (Oberhasli or Saanen), in their third or fourth parturition, were randomly assigned to groups based on litter size (single or twin) and day of slaughter (50, 80, 110 and 140 DOP) in a fully factorial design. Net mineral accretion for pregnancy did not differ by goat breed. The total daily Ca, P, Mg, Na and K requirements for pregnancy were greatest in goats carrying twins (P<0.05), and the requirements increased as pregnancy progressed. Experiment 2 was performed to estimate net Ca, P, Mg, Na and K requirements for dairy goat maintenance during pregnancy. In total, 58 dairy goats (Oberhasli and Saanen) carrying twin fetuses were assigned to groups based on slaughter day (80, 110 and 140 DOP) and feed restriction (ad libitum, 20% and 40% feed restriction) in a randomized block design. The net Ca, P and Mg requirements for maintenance did not vary by breed or over the course of pregnancy. The daily net requirements of Ca, P and Mg for maintenance were 60.4, 31.1 and 2.42 mg/kg live BW (LBW), respectively. The daily net Na requirement for maintenance was greater in Saanen goats (11.8 mg/kg LBW) than in Oberhasli goats (8.96 mg/kg LBW; P<0.05). Daily net K requirements increased as pregnancy progressed from 8.73 to 15.4 mg/kg LBW (P<0.01). The findings of this study will guide design of diets with adequate mineral content for pregnant goats throughout their pregnancy.
Meta-analyses of experimental data in animal nutrition⋆
- D. Sauvant, P. Schmidely, J. J. Daudin, N. R. St-Pierre
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Research in animal sciences, especially nutrition, increasingly requires processing and modeling of databases. In certain areas of research, the number of publications and results per publications is increasing, thus periodically requiring quantitative summarizations of literature data. In such instances, statistical methods dealing with the analysis of summary (literature) data, known as meta-analyses, must be used. The implementation of a meta-analysis is done in several phases. The first phase concerns the definition of the study objectives and the identification of the criteria to be used in the selection of prior publications to be used in the construction of the database. Publications must be scrupulously evaluated before being entered into the database. During this phase, it is important to carefully encode each record with pertinent descriptive attributes (experiments, treatments, etc.) to serve as important reference points for the rest of the analysis. Databases from literature data are inherently unbalanced statistically, leading to considerable analytical and interpretation difficulties; missing data are frequent, and data structures are not the outcomes of a classical experimental system. An initial graphical examination of the data is recommended to enhance a global view as well as to identify specific relationships to be investigated. This phase is followed by a study of the meta-system made up of the database to be interpreted. These steps condition the definition of the applied statistical model. Variance decomposition must account for inter- and intrastudy sources; dependent and independent variables must be identified either as discrete (qualitative) or continuous (quantitative). Effects must be defined as either fixed or random. Often, observations must be weighed to account for differences in the precision of the reported means. Once model parameters are estimated, extensive analyses of residual variations must be performed. The roles of the different treatments and studies in the results obtained must be identified. Often, this requires returning to an earlier step in the process. Thus, meta-analyses have inherent heuristic qualities.