Milk Yield and Composition of Mixed-Breed Goats on Rangeland during the Dry Season and the Effect on the Growth of Their Progeny

Methionine Supplementation during Pregnancy of Goats Improves Kids' Birth Weight, Body Mass Index, and Postnatal Growth Pattern

The last third of gestation is a period of high energy and protein demand for the dam to support fetal growth and the following onset of lactation. Methionine is an essential amino acid that contributes to protein formation, fetal development, and milk synthesis; thus, is likely to have positive effects on the weight and size of the newborn and, afterward, milk yield and milk composition, which may improve growth patterns of the progeny. To test these hypotheses, we used 60 pregnant multiparous Alpine goats with similar live weights and gestational ages (~Day 100 of pregnancy; Mean ± SD; 1410 ± 14 days old and 50.4 ± 6.6 kg) and were separated into two groups: control and supplemented with the delivery. Treatments were T-MET (n = 30; received 1% herbal methionine Optimethione® dry matter based on from Day 100 of the pregnancy to delivery) or T-CTL (n = 30; served as the control and did not receive methionine). The methionine powder provided individual supplementation and was adjusted every week as the live weight and dry matter intake changed. At birth, the weight, body mass index (BMI), birth type, and sex of the kids were determined. Subsequently, the progeny was weighed weekly up to weaning. Two weeks after parturition, the milk composition was recorded weekly, and the milk yield was recorded monthly. The maternal live weight at the start (Mean ± SEM; T-CTL: 50.5 ± 1.1 vs. T-MET: 50.3 ± 1.3 kg) and end (T-CTL: 54.2 ± 1.3 vs. T-MET: 52.8 ± 1.4 kg) of the experiment did not differ statistically among treatments (p > 0.05); however, daily live weight changes tended to differ between groups (T-CTL: 73 ± 10 vs. T-MET: 51 ± 7 g day−1; p = 0.06). The birth weight (T-CTL: 3.1 ± 0.1 vs. T-MET: 3.5 ± 0.1 kg; p < 0.001), daily live weight change (T-CTL: 121 ± 6 vs. T-MET: 141 ± 6 g day−1; p < 0.01), and weaning weight (T-CTL: 8.3 ± 0.2 vs. T-MET: 9.3 ± 0.3 kg; p < 0.01) differed between treatments. The BMI at birth (T-CTL: 0.28 ± 0.01 vs. T-MET: 0.3 ± 0.01 units kg m−2; p < 0.01) and at weaning (T-CTL: 0.85 ± 0.1 kg vs. T-MET: 1.00 ± 0.06 units kg m−2; p < 0.05) differed between treatments. Milk components (protein, fat, lactose, and solids non-fat) and milk yield were similar between treatments (p > 0.05). It is concluded that the inclusion of methionine in the maternal goat diet during the last third of gestation increases the birth and growth variables of the progeny but without significant influence on the milk yield and composition.

Effect of Replacing Sorghum Stubble with Tillandsia recurvata (L.) on Liveweight Change, Blood Metabolites, and Hematic Biometry of Goats

Tillandsia recurvata is an epiphyte that grows on the canopy of many trees in tropical and subtropical areas of America. The objective of this study was to evaluate the effect of partial or complete substitution of sorghum stubble with T. recurvata on liveweight change, metabolic profile, and complete blood count of goats fed increasing levels (0, 30, and 60%, dry matter basis) of T. recurvata. Thirty non-pregnant three-year-old, non-lactating, healthy mixed-breed goats, ten animals per treatment (T0, T30, and T60), were adapted to diets and facilities for 14 days (d-14). Blood samples were collected at d-15, 28, and 56. At the last phase of the trial (from days 67 on), control goats tended to gain more (p = 0.09) weight than their counterparts consuming T. recurvata. Plasma protein, glucose, triglycerides, calcium, and phosphorus concentrations did not differ among dietary treatments (p > 0.05). Dietary treatment influenced red blood cells (higher for T60; p < 0.01), white blood cells (higher for T30; p < 0.05), mean corpuscular volume (higher for T0; p < 0.001), and mean corpuscular hemoglobin concentration (higher for T0; p < 0.01), although not the rest of the blood variables (p > 0.05). The hematocrit percentage tended to be higher (p = 0.06) in T30 than T0 and T60. It was concluded that replacing sorghum stubble with T. recurvata did not modify the metabolic status and maintained live weight of goats. Nevertheless, the use of T. recurvata as feed for goats would improve the nutrition of these animals in the dry season compared to the current diet obtained from an arid rangeland, reducing production costs, and would alleviate the damage caused by this aggressive epiphyte to host trees.

Nutrient requirement equations for Indian goat by multiple regression analysis and least cost ration formulation using a linear and non-linear stochastic model

An elaborate multiple regression analysis was done to arrive a nutrient requirement equation for goat including dry matter intake, DMI (kg/day), total digestible nutrient, TDN (g/day) and crude protein, CP (g/day) based on animal body weight (BW)(kg) and average daily gain (ADG)(g/day). The derived equations were highly significant (p < 0.001) and had high R² (0.99) values. The estimated values of TDN, CP and DMI are compared with NRC (1981), Kearl (Nutrient Requirements of Ruminants in Developing Countries, All Graduate Theses and Dissertations, 1982), as well as ICAR (Livestock Management, 2013). The estimated total TDN and CP requirements at different body weights and ADG are close to the values of recommended feeding standards of Mandal et al. (Small Ruminant Res., 58, 2005, 201). The estimated DMI values are close to the values of ICAR (Livestock Management, 2013) but lower (26.5%-43.8%) as compared to NRC (1981). Regressed values are used to develop a linear programming (LP) model and a stochastic model (SM) for least-cost ration formulation for the Indian goat breed, whose average BW is about 45 kg and ADG is 130 (g/day), and which is solved using LP simplex and Generalised Reduced Gradient (GRG) nonlinear of Microsoft Excel. The models satisfy the nutrient requirement calculated by regression equations with minimum specified level of variation (usually 5%-10%) in CP and TDN. Both methods adequately meet the nutritional requirements. Therefore, an electronic sheet is developed in Excel to calculate DMI, TDN and CP for different body weights, ADG and formulate the ration by LP and stochastic model.