The effects of nutrition and parity on the development and productivity of Angora goats: 3. Effects of six combinations of mid pregnancy and postnatal nutrition on udder development, lactation, milk composition and net energy of milk production
Introduction
Angora goats in Turkey have been traditionally kept by nomadic pastoralists for their milk, meat, mohair, leather and other social needs. From a livelihood perspective, cost benefit studies in Turkey indicate that milk production still represents 39% of the value of production from Angora goat enterprises (Çelik and Bayramoğlu, 2010). Daskiran et al. (2010) surveyed 100 Angora farms in 5 districts of the Ankara province. Based on interviews they reported daily Angora goat milk production varied significantly between districts with an average of 1.83 kg/day (range 1.43–2.19 kg/day).
Little empirical research has been conducted on the nutrient requirements for breeding and lactating Angora goats. Together, Shelton (1993), who summarised his long experience researching Angora goats in Texas, and van der Westhuysen et al. (1988), who provide the industry wisdom for the South African mohair industry, provide evidence from only one study on the lactation performance of Angora does. Thus, recommendations for the nutrient management of lactating Angora goats are mostly based on theoretical calculations, and more generally NRC (1981) and Luo et al. (2004). In their detailed analysis of nutrient requirements for goats, Luo et al. (2004) specifically excluded lactating Angora does from their modelling given the dearth of reports. Thus, what constitutes normal lactation for Angora goats and the effects of nutritional manipulation during pregnancy and lactation have not been adequately investigated. In providing advice on the nutritional requirements of breeding Angora goats to Texan breeders, and in the absence of any objective data, Huston et al. (1971) assumed milk production of 0.68 kg/d with a composition of 4.5% fat and 3.5% protein. This milk yield is much lower than those typically obtained with specialist dairy goat breeds (Gipson and Grossman, 1989), although the composition of dairy goat breeds is typically about 3.8% fat (Jenness, 1980).
The lactation curve of dairy goat breeds typically extend to about 300 days. Daily milk yield is reported to increase from kidding time to peak at 4–6 weeks postpartum before a long and steady decline. Both peak and total milk production in dairy goats differs between breeds and individual goats, is greater for twin parity compared with single parity rearing does and can be affected by nutritional manipulation and seasonal conditions (Morand-Fehr et al., 1991, Morand-Fehr et al., 2007). There are some well-known influences of farming and feeding practices on the lactation performance of dairy goats. It is clear from studies with dairy goats and breeding sheep and cattle that energy intake is the prime factor affecting lactation performance and offspring productivity (SCA, 1990). Daily milk production and peak production depend upon the energy density of the diet during the last trimester of gestation (Sahlu et al., 1995) and the optimal energy density varies during lactation (Morand-Fehr and Sauvant, 1978a, Morand-Fehr and Sauvant, 1978b). Morand-Fehr et al. (1991) described the interdependence of 12 factors which influence milk composition including level of production, nutrition, milking practices, season, photoperiod, climate, pathological issues, genetics and stage of lactation. Generally, with dairy goats, there is an inverse correlation between daily milk production and the concentration of milk solids.
During pregnancy, the requirements for the growth of tissues varies as the pregnancy progresses. Placental growth is completed by 100 days (Redmer et al., 2004). Mammary gland development occurs in the last trimester of pregnancy, particularly the last 4 weeks (Rattray et al., 1974). Jenness (1980) recommended that udder measurements be used in breeding programs for dairy goats given their association with milk yield. In Toggenburg dairy goats, udder characteristics are significantly correlated with milk yield and were highly heritable (Wang, 1989). Anderson and Wahab (1990) described the changes in udder growth and development during pregnancy and lactation in breeds of dairy goat. Stroma, which are the supportive structures in the mammary gland represent 60% of udder weight in early pregnancy but only 24% in late pregnancy, while milk producing parenchyma cells represent 40% of the gland in early pregnancy and 76% at 145 days of pregnancy. Lérias et al. (2014) reviewed the mammary gland morphological patterns underlying milk production during the lactation cycle for goats. No information could be located on udder characteristics of Angora goats and their relationship with milk production, parity and nutrition management.
It is clear from a review of the literature that the production and composition of Angora goat milk is poorly characterised, the net energy requirements for milk production are unknown and the responses of lactating Angora goats to nutritional manipulation are poorly quantified. Research in southern Australia on annual pastures has also shown that non-breeding Angora goats face energy restrictions from summer to winter (January to August) (McGregor, 2010). Given that most breeding Angora goats are managed to kid during the period May to September (winter-spring), it is highly likely they will experience energy restrictions given the physiological requirements for pregnancy and lactation. A series of investigations into the effects of both mid pregnancy and postnatal nutrition of Angora does on productivity and kid development were conducted using a replicated experiment. This report is focussed on milk production and composition, energy requirements for milk production and udder development.
Section snippets
Design and nutrition treatments
The design was 3 levels of mid pregnancy (MPN) × 2 levels of postnatal nutrition (PNN) providing 6 nutrition patterns. Replicates were 17 randomised live weight and parity blocks each of 6 does. Does were randomly allocated to one of the 6 treatment combinations prior to the experiment commencing. The total of 102 does included 24 twin bearing does. MPN treatments were implemented from days 47–105 after conception as follows:
Control (C) – fed to mimic the usual live weight loses of goats and
Milk production
The mean and ranges in milk production and milk solid composition are summarised in Table 1. Average milk production was highest one week after kidding compared with later stages of lactation, declining from 2.16 kg/d at day 8 to 0.83 kg/d at day 90. Fat corrected milk production declined from 3.99 kg/d at day 8 to 1.38 kg/d at day 90. The maximum milk production for individual does remained similar between days 8 and 90 of lactation.
Milk production was affected by PNN at days 21 and 42 of
Milk production
Maximum daily milk production occurred on day 8 of lactation. Milk production then declined to relatively constant levels between days 21–42 before declining to day 90. This finding should not be surprising as Anderson et al. (1981) demonstrated that peak numbers of milk producing cells in dairy goats are reached by day 5 of lactation. However, these findings differ from the accepted shape of the daily milk production curve in dairy goats, which is an increase to a peak at about 6–8 weeks,
Conclusions
The main factors affecting milk production were postnatal nutritional practices and parity, while postnatal nutrition also had some effects on milk composition and milk energy value. Better fed goats and does rearing twins produced more milk compared with restricted fed does and does rearing single kids. Both milk production and milk fat composition were substantially greater than earlier reports for Angora does. Generally, the lactation peaked at day 8 and declined thereafter, although it
Acknowledgements
The Victorian Department of Primary Industries, the Rural Industries Research and Development Corporation (Project DAV42A) and the Australian Mohair Research Foundation Ltd. funded this project. Without the direct support of Mr Ken Slatter this project would not have been possible. Mr K.L. Butler provided biometric advice. DPI colleagues are thanked for assistance particularly Ms Andrea Howse, Messrs Brendan Scott and Brian Hester.
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