greater kidding and attributed it to the occurrence of the lactation peak very close to parturition, which therefore could not be recognized by the mathematical func- tion used. Reasons for the atypical lactation curve are: (i) biological differences between animals; (ii) mathematical properties of the model used; and (iii) the already mentioned structure of the data analysed (mainly the distance from par- turition of the first record available and the distribution of records throughout lactation). The main consequence of an atypical shape is the change in sign and, therefore, in meaning of lactation curve parameters. This makes the interpretation of outcomes very difficult when individual values of lactation curve parameters are analysed with linear models to estimate the effect of fixed and random factors on lactation curve shape traits (Shankset al., 1981), or when mathematical func- tions are used to fit individual curves in random regression test day models (Jamrozik and Schaeffer, 1997).
Finally, although most studies on goat lactation curve modelling have dealt with milk yield, some work on modelling of the lactation pattern of milk components has also been done. For example, Rotaet al. (1993) fitted the Wood function to curves for fat and protein content and for SCC in Verata goats, obtaining good fitting performances also for SCC (R2= 0.97).
first-kidding goats from higher parities (Fig. 2.4) highlights the difference in pro- duction level among different parities. The different curves reported in Fig. 2.5 highlight the higher production level of goats with two kids at parturition. More- over, it can also be observed that the effect of type of kidding is essentially concentrated in the first 120 days of lactation.
Besides the visual inspection of the estimated curve, the functional approach allows evaluation of the effect of different sources of variation on the lactation curve by analysing values of function parameters estimated for each individual pattern (Gipson and Grossman, 1989; Wahomeet al., 1994; Giacconeet al., 1995;
0 0.5 1.0 1.5 2.0
0 50 100 150 200
Days in milk
Milk yield (kg/day)
1 2 3
Fig. 2.4. Average lactation curves for milk yield of Sarda breed goats of different parities estimated with the Wood model.
0 0.5 1.0 1.5 2.0
0 50 100 150 200
Days in milk
Milk yield (kg/day)
1 2
Fig. 2.5. Average lactation curves for milk yield of Sarda breed goats with different number of kids at parturition estimated by the Wood model.
Ruvunaet al., 1995; Montaldoet al., 1997; Akpaet al., 2001; McManuset al., 2003). As an example, Table 2.5 reports least squares means for different pari- ties of the parameters of the Wood function fitted to individual lactation curves of Derivata di Siria (Giaccone et al., 1995) and Red Sokoto (Akpaet al., 2001) goats. In both studies, a statistically significant effect of parity on parameterawas found, with increasing values going from primiparous to higher parities. This was expected since parameterais responsible for the height of the curve, i.e. the level of production, which is generally lower in young animals. The effect of parity on parameterbreported by Giacconeet al. (1995) can be explained by a more pro- nounced curvature of the lactation curve in the first phase of lactation due to higher peak productions. Finally, the absolute value of parameterc, which has a minus sign, was lowest for first-kidding goats, evidencing the higher persistency of young animals.
However, a drawback of using a mathematical function of time to estimate the effect of non-genetic factors on the lactation curve is that it assumes a constant effect of each factor during the whole lactation, whereas there are effects, such as variation in feeds, climate conditions or health status, that may affect only part of the lactation period (Stantonet al., 1992). As an alternative, short-term environ- mental effects can be conveniently modelled with the so-called test day models (Stantonet al., 1992). These linear mixed models include a test date factor, which models effects observed at each date in which production is measured, and a DIM factor, whose estimates allow the construction of the lactation curve corrected by the effects of other factors included in the model. Test day models analyse test day milk yields according to a slit-plot in time statistical design (Diggleet al., 2002), where the animal is the main plot and the different time intervals at which measurements are taken are the subplots (Macciottaet al., 2004b).
Parameter
a b c Reference
Paritya Akpaet al. (2001)
1 0.388 0.338 0.017
2 0.471 0.355 0.018
3 0.877 0.323 0.017
P≤0.05 NS NS
Parityb Giacconeet al.
(1995)
1 0.116 0.115 0.0029
2 0.119 0.153 0.0031
3 0.129 0.163 0.0045
P≤0.01 P≤0.01 P≤0.01
NS, not significant.
aRed Sokoto.
bDerivata di Siria.
Table 2.5. Least squares means of individual values of Wood parameters for different parities in goats.
In goats, test day models have been used to estimate lactation curves in Murciano-Granadina, Girgentana and Sarda breeds (Todaro et al., 1999;
Fernándezet al., 2002; Macciottaet al., 2005a). Figures 2.6, 2.7 and 2.8 report average lactation curves for milk yield, fat and protein content, respectively, for Sarda breed goats of three different parities, estimated with a test day model.
Lactation curves estimated by test day models are less regular and are char- acterized by the absence of the lactation peak in comparison with those reported in Figs 2.4 and 2.5. Actually, in the functional approach, the effect of lactation stage on milk yield is modelled with functions originally conceived to describe the typical shape of the lactation curve (Fig. 2.1), which tend to reconstruct the increasing phase until the lactation peak, even in the case of poor availability of data. By contrast, the use of DIM intervals in test day models to fit the effect
0 0.5 1.0 1.5 2.0 2.5
0 1 2 3 4 5 6 7 8
Months of lactation
Milk yield (kg/day)
1 2 3
Fig. 2.6. Average lactation curves for milk yield of Sarda breed goats of different parities estimated with a test day model.
4.0 4.5 5.0 5.5 6.0
0 1 2 3 4 5 6 7 8
Days in milk
Fat content (%)
1 2 3
Fig. 2.7. Average lactation curves for milk fat content of Sarda breed goats of different parities estimated with a test day model.
of lactation stage allows for a greater flexibility and, therefore, waves in the middle of lactation may also occur (Druetet al., 2003).
Lactation curves for fat and protein content show an opposite trend in com- parison with milk yield. A more regular pattern for protein content in comparison with fat can be observed in goats, as observed previously in other ruminants.
Finally, an animal random factor is included in test day models, in order to account for individual variability. The ratio between the variance component per- taining to the animal factor and the total phenotypic variance (animal + residual) represents the average correlation among daily productions within each lactation, or repeatability. In Sarda goats, this ratio has been estimated to be 0.34, 0.17 and 0.45 for milk yield, fat and protein content, respectively (Macciottaet al., 2005a).