A study was carried out to evaluate how the energy level of the diet can affect milk production and quality in Girgentana lactating goats in relation to polymorphism at the alphas1-casein (CSN1S1) genotype locus. Twenty-seven goats, homogeneous for milk production (1.5+/-0.3 kg/d), days of lactation (90+/-10 d) and body weight (35.8+/-5.5 kg) were selected on the basis of their CSN1S1 genotype, as follows: nine goats homozygous for strong (AA) alleles, nine goats homozygous for weak alleles (FF) and nine goats heterozygous (AF). The goats were used in a 3x3 factorial arrangement of treatments, with three genotypes (AA, FF, AF) and three diets at different energy levels (100%, 65% and 30% of hay inclusion). The experiment consisted of three simultaneous 3x3 Latin squares for the three genotypes, with one square for each level of hay inclusion in the diet. All the animals were housed in individual pens. Each experimental period lasted 23 d and consisted of 15 d for adaptation and 8 d for data and sample collection, during which the goats received the scheduled diet ad libitum. The animals were fed three different diets designed to have the same crude protein content (about 15%) but different energy levels: a pelleted alfalfa hay (H100) and two feeds including 65% (H65) and 30% (H30) of alfalfa hay (respectively 1099, 1386 and 1590 kcal NE for lactation/kg DM). All the diets were ground and pelleted (6 mm diameter). AA goats were more productive than AF and FF goats (respectively: 1419 v. 1145 and 1014 g/d; P=0.002). Indeed the interaction energy levelxgenotype was significant (P=0.018): in fact AA goats showed their milk increase only when fed with concentrates. Differences in protein and in casein levels between the three genotypes were in line with results expected from the different allele contribution to alphas1-casein synthesis. Milk urea levels were significantly lower in AA goats compared with AF and FF genotypes (respectively 32.7 v. 40.4 and 40.4 mg/dl; P=0.049) and significantly lower when goats were fed with 65H and 30H diets than with 100H diet (respectively 37.4 and 34.3 v. 41.7 mg/dl; P<0.001). Indeed, a significant interaction genotypexdiet (P=0.043) occurred for milk urea, which was significantly lower in AA goats but only when fed with concentrates (65H and 30H). Blood concentrations of energy indicators (glucose, non-esterified fatty acids and beta-hydroxybutyric acid) were not influenced by genotype. The results confirm that strong alleles are associated with a greater efficiency of feed utilization and seem to show that a high energy level of the diet can further improve this efficiency.

-This review deals with the most relevant limits and developments of the modeling of intake of sheep and goats reared intensively and extensively. Because small ruminants are normally fed ad libitum, voluntary feed intake is crucial in feeding tactics and strategies aimed at optimal animal production. The effects of genetic, neuroendocrine, hormonal, feed and environmental factors on voluntary feed intake were discussed. Then, several mathematical models to estimate dry matter intake (DMI) were examined, with emphasis on empirical models for sheep and goats in intensive farm systems or in extensive areas under pasture or rangeland conditions. A sensitivity analysis of four models of prediction of DMI in housed lactating dairy sheep and meat sheep breeds was also presented. This work evidenced a large variability in the approaches used and in the variables considered for housed sheep and goats. Regarding the estimation of feed intake for grazing sheep and browsing goats, the accuracy of estimates based on empirical models developed so far is very low when applied out of the boundaries of the studied system. Feeding experiments indoors and outdoors remain fundamental for a better modeling and understanding of the interactions between feeds and small ruminants. However, there is a need for biological and theoretical frameworks in which these experiments should be carried out, so that appropriate empirical or mechanistic equations to predict DMI could be developed.

No abstract

No abstract

No abstract