Pasture intake, as expected, was negatively correlated with diet supplements, due to the substitution effect that the latter generally cause on herbage consump- tion. Such correlation was higher when supplement intake was expressed as grams of CP than when expressed as grams of DM. This had already been observed in sheep and was attributed to their ability to self-regulate intake on the basis of their protein needs (Avondoet al., 2002). Similar results were also found in trials on rams (Avondoet al., 2004a) and goats (Fedeleet al., 1993). Recently, the ability of lactating goats to reduce protein intake from pasture when receiving supple- ments rich in CP was clearly demonstrated (Avondoet al., 2004b). Thus, since goats are highly selective and choose parts of the pasture rich in protein even in very poor feeding conditions, the negative effects of protein supplement on pas- ture intake should be attributed, with greater likelihood, to the self-regulating protein theory hypothesized for sheep.
In our statistical analyses, all of the variables that were not co-associated, and which were significantly correlated with intake and were biologically mean- ingful, were included in multiple regression prediction models. The most signifi- cant prediction equation (R2= 0.41;P< 0.01) is as follows:
Pasture intake (g DM/day) = 822.11 – 6.188CPS + 0.138FCM + 9.131BW where CPS is the quantity of CP given with supplement (g/day), FCM is the pro- duction of 5% fat-corrected milk (g/day) (Pulinaet al., 1991) and BW is animal body weight (kg).
Pasture characteristics did not have significant effects on goat intake capacity in our study. Indeed, Garcíaet al. (1995) observed an increase in goat pasture intake when NDF and lignin content increased. In fact, the poor quality of the pas- ture is usually not a limiting factor for intake, thanks to the abundant rumen microflora of goats, associated with their intense selective activity. Only in extreme conditions, such as on a pasture containing more than 30% of DM and with a very poor chemical–nutritive composition, was pasture utilization by goats limited.
Figure 7.5 shows that DM intake at pasture tends to decrease as DM percentage of herbage increases, particularly over 30%. In these conditions, none of the vari- ables was significantly correlated with intake levels and it was clear that dietary supplements did not have any substitution effect, as normally occurs in good pasture conditions. It is well known that if the herbage is poor in quality, the administration of a supplement does not reduce intake but, in certain circum- stances, can actually increase it. Since only a few data on herbage DM content over 30% were available, a specific equation could not be developed. For this reason, it is recommended to use the equation proposed above only for pastures with DM content below 30%.
including fibre and dwarf breeds. Sahluet al. (2004), on the basis of the equations proposed by Luoet al. (2004), have developed a detailed table reporting the predicted intake values for stall-bred kids belonging to meat breeds, dairy breeds (Saanen, Alpine, Damascus, Norwegian, Swedish Landrace and dairy cross- breeds) and other breeds described as ‘indigenous’. Estimated intake values vary with energy concentration of the diet, mean daily weight gain and BW of the ani- mals. Table 7.4 is a simplified version of the table proposed by Sahlu et al.
(2004), which includes estimated values for DM intake of kids of dairy goats, with different energy concentrations of the diet and a daily weight gain of 100 g. It can be seen that, for equal BW, intake diminishes as the energy concentration of the diet increases. Moreover, even at the lowest energy concentration, the intake estimated by Sahlu et al. (2004) for dairy breeds is always lower than that reported by the Institut National de la Recherche Agronomique (INRA) (Morand-Fehr and Sauvant, 1988).
Goetschet al. (2003) found that total DM intake was not affected by feeding management (free-choice availability of concentrate and forage versus mixed diet) in Alpine doelings. However, separate free-choice offering of concentrate and forage increases concentrate and CP daily intake and conversion efficiency.
The equations proposed by Lu and Potchoiba (1990) and by Luoet al. (2004) to estimate intake are rather complex as they presuppose the availability of chemi- cal analysis of feed supplied, to calculate the energy concentration of the diet.
This complicates their practical application at farm level, particularly in extensive systems in which the characteristics of feed resources vary widely over the year.
Moreover, Mediterranean breeds are poorly represented among the genetic types taken into consideration by Luoet al. (2004), since out of the 50 trials from which data on growing animals were obtained, only about ten concerned Medi- terranean breeds or populations. Therefore, an attempt was made to develop
50 45 40 35 30 25 20 15 10 5 0 2000 1800 1600 1400 1200 1000 800 600 400 200
Herbage DM (%)
Herbage intake (g DM/day)
Fig. 7.5. Regression between pasture intake of lactating goats and herbage dry matter (DM) content.
equations to estimate DM intake using only BW as predictor, based on 87 intake means from different experiments on Mediterranean and African breeds, accord- ing to the classification of Flamant and Morand-Fehr (1982). Table 7.5 provides an overview of the diet adopted in each experimental trial of the database.
The correlation between DM intake and BW was significant but very low (r= 0.21;P< 0.05). In Table 7.6, which reports the mean DM intake per class of live weight of our database, DM intake tends to increase between 10 and 20 kg of live weight; subsequently, however, the intake level diminishes. This trend can be attributed to different feeding conditions of the animals. In this small
Energy concentration of the diet (MJ/kg)
Live weight (kg) 7 9 11 13 INRA
10 – 430 380 340 –
15 750 650 580 530 900
20 940 800 720 660 1040
25 1090 930 830 760 1110
Table 7.4. Estimated values of dry matter intake (g/day) in kids of dairy goats during growth (100 g/day), in relation to diet energy concentration (calculated by Sahlu et al., 2004). Comparison with data from Institut National de la Recherche Agronomique (INRA, 1988).
Breed Diet Reference
Damascus Hay and concentrate Hadjipanayiotou (1995) Nigerian local Concentrate and by-products Aregheore (1996) Greek local Carpinus orientalis or
Fraxinus ornus leaves
Papachristou (1996) Spanish Phaseolus vulgaris straw and
hay orAcacia leaves (rigidula orfarnesiana)
Ramírez and
Ledezma-Torres (1997) Barbari Straw and whole soy seeds Mani and Chandra (2003) Barbari Hay and concentrate
administered as such and as feed block
Samanthaet al. (2003)
Local Indian Straw and concentrate Anbarasuet al. (2004) Anglo-Nubian × Fiji Ischaemum aristacum and
Ipomoea batatas green forage
Aregheore (2004) Spanish Hay and concentrate Joematet al. (2004)
Spanish Hay and concentrate Urgeet al. (2004)
Girgentana Hay and concentrate Avondo (personal communication, 2005) Table 7.5. Experiments used for the database created to predict the dry matter intake of growing goats.
database, the effects of the chemical composition of the diets, within each weight class, could not be tested appropriately. Nevertheless, it could be observed that mean intake in the weight classes above 20 kg was lower than in lighter weight classes, probably due to a particularly high mean dietary NDF concentration (55% between 20.1 and 25 kg of live weight) or, on the contrary, due to an excessive concentrate level in the diet (79% of concentrates for live weights over 25 kg). Moreover, the kids classified as ‘Mediterranean’ include breeds that differ among themselves and could have different intake and digestive capacity of poor-quality forage. In this regard, Silanikove (1986), Lu and Potchoiba (1990) and Urgeet al. (2004) demonstrated breed effects on intake.
In conclusion, the prediction of intake of growing goats of Mediterranean breeds using the database created from literature data was unfeasible, because of the wide variability of the breeds and the diets considered in the experimental trials. Therefore, further experimental data or different approaches are required.