Results of the dynamic simulation for a range o f cows i n different environments are demonstrated in Figure 3 , and the accumulated values of the 270-day lactation are
Cha pter 8 . . . .
presented in Table 3 . The THIN cow simulated was BCS of 4.0 at parturition. Milk production in early lactation was depressed compared to the other simulation cows, as the THIN cow did not have the fat reserves to support high levels o f milk production. Broster and Broster ( 1 998) found cows of low BCS at parturition were associated in early lactation with increased feed intakes, generally reduced milk yield, and a reduction in BCS loss, or at very low BCS, gains in BCS compared to cows of medium to high BCS at parturition which is consistent with the simulated results of the THIN cow. The model recognises a cow will not reduce its body condition or fatness below a certain amount, but this level can vary depending on genotype (i.e. estimated breeding value for BCS). The amount of body fat mobilisation by a cow in early lactation also depends on the amount of body fat present at parturition.
Offering supplements at the rate of 2 kg OM per day throughout lactation (SUPP cow) resulted in an increase in total milk yield of 3 1 6 kg compared to the BASE cow, equivalent to 0.59 kg milk/kg OM supplement. The nutritional effect is simulated through the calculation of relative intake, which increases with the addition of more supplements. The simulated response to supplements is between the values of 0.55 and 0.67 kg milk/kg O M concentrate for NZ Holstein Friesian genotypes in pastoral based systems reported by Horan et al (2005) in Ireland, and Ko lver et al. (2005) in NZ. The SUPP cow also ended the lactation at a higher BCS than the other simulation cows, and was equivalent to 0.00045 BCS units/kg OM concentrate. The simulated value is at the low end of the scale presented by Kolver et al. (2005), who quantified the values at 0.00 1 0 and 0.0006 BCS units/kg DM concentrate for NZF and OHF genotypes, respectively. The simulated value is, however, similar to the value o f 0.00033 BCS units/kg OM concentrate which was calculated for the NZF genotype based on results presented by Horan et al. (2005), adopting the Irish to NZ BCS conversion equation of Roche et al. (2004). The substitution rate of pasture was equivalent to 0.59 kg OM/kg OM supplement offered, which is similar to values of 0.55 to 0.68 kg OM/kg OM supplement offered that were obtained when applying the equations of previous studies presented by Stockdale (2000b).
35
�
30 � 25 0 u�
20 '--' :!2 1 5 0) '>., � 1 0 � 5 0 ,---... 0 30 60 - - - - HGM S Upp -- BASE -... _--- - ---- T H I N '---'--,--- 90 1 20 1 50 1 80 2 1 0 240 270 Days in milk ---- 20 >-. (\3 "0 1 8 � 0 u 1 6�
'--' 1 4 0) � 1 2 .... � 1 0 (ii E 8 >-. .... Cl 6 0 - - - - HGM • • • • • • • SUPP -- BASE ---, ___ -.-. - THI N --- 30 60 90 1 20 1 5 0 1 80 2 1 0 240 270 Days in milk - - - - 5 .00 en�
4 . 5 0 4.00 ._--- - ---_.----., . ..,.. S UPP BASE T H I N _ .... -' o 30 60 90 1 20 1 5 0 1 80 2 1 0 240 270 Days in milkFigure 3: Dynamic demonstration of the simulation model in terms of milk yield, DM intake and BCS throughout lactation for BASE, THIN, SUPP and HGM
simulation cows. c � < � o '0 3 � ::J - o - 3: o o Cl) 3:
Chapter 8 . . . .
Table 2 : Accumulated 270-day yields of milk, fat and protein, average fat and protein concentration, season end BCS and DM intake for the simulation cows BASE, THIN, SUPP and HGM.
Concentration Season 270-day DM
Milk Fat Protein Fat Protein end BeS intake (kg)
BASE 5,474 256 1 9 1 4.68 3 .49 4.52 3,969
THIN 5,400 253 1 89 4.69 3 .49 4.29 4,0 1 5
SUpp 5,790 270 203 4.66 3.5 1 4.76 4, 1 37
HGM 6,554 287 2 1 7 4.37 3.32 4.52 4,353
S imulated milk yield for the HGM cow was 1 ,080 kg milk higher than the BASE cow; effectively a 2. 1 6 kg increase in milk per kg increase in milk EBV which is greater than the theoretical expectation of a 1 kg increase in milk per kg increase in milk EBV. To compare the present result with a previous NZ result, Bryant et al. (2003) quantified the phenotypic benefit of genetic gain at 0.89 to 1 .78 kg milk per kg increase in milk EBV at a low feeding level (equivalent to a typical NZ environment). Whereas at a high feeding level, but probably still below the feeding level simulated in the present study, the corresponding value was 1 . 86 to 2.0 1 kg milk per kg increase in milk EBV. Hence, the result is not unrealistic and demonstrates a scaling effect where the phenotypic difference between low and high genetic merit animals are greater at high than low feeding levels (Veerkamp et al.
1 994).
The BASE and HGM cows were offered the same amount of feed, yet a greater feeding drive, equivalent to an increase in 270-day and daily OM intake of 384 and 1 .42 kg OM, respectively, was simulated in the HGM compared to the BASE cow. In the present model, feed intake is driven by mammary cell numbers with mammary cell numbers greatest in cows of high genetic merit. The higher daily OM intake in the HGM compared to BASE cow is equivalent to 0.0028 kg OM/kg milk EBV, and is similar to values estimated from Kennedy et al. (2003a) in Ireland where daily intakes of pasture and concentrates were increased by 0.0020, 0.002 1 and 0.0030 kg DM/kg milk EBV in low, medium and high concentrate feeding level systems in early lactation. There was no difference in BCS throughout lactation between the BASE and HGM cows, as both cows had the same EBV for BCS, feeding levels, and BCS at parturition (Figure 3).
. . . Development of MOOSIM
Results of the simulations from days 50 to 60 of lactation, where different environmental scenarios were imposed, are presented in Table 3 . Hot conditions suppressed OM intake and MS yield and resulted in more marked live weight reductions in all cows. Hot conditions had a marked effect on MS yield in the HGM cow, in agreement with the simulation results presented by Berman (2005). The reduced OM intake, and hence greater live weight loss, in heat stressed cows is consistent with the fmdings of West et al. (2003). Poor pasture quality and rolling terrains resulted in a marked reduction in MS yields, greater live weight losses, similar OM intakes, but marked reductions in ME intake and reduced the efficiency of converting feed into milk (Equation L40 of Appendix 2).
Additional supplements increased MS yield and DM intake, reduced the degree o f live weight loss o r resulted in the cow being in positive energy balance (Table 3). The HGM (0.78 kg milk/kg OM supplement or 54 g MS/kg OM supplement) and
Table 3: The effect of hot conditions, poor pasture quality and additional supplements on milk yield, MS yield and live weight change for the BASE, THIN, SUPP and HGM simulation cows from days 50 to 60 of lactation.
DM intake MS Live
Predicted Change Predicted Change Predicted Change
(%) (%)
Original Predicted Values
BASE 1 6.34 - 1 .83 - -0.06 -
THIN 1 6.55 - 1 .84 - +0.05 -
SUPP 1 7.01 - 1 .92 - -0.03 -
HGM 1 7.52 - 1 .97 - -0.06 -
Hot conditions: 2ye and 90% humidity
BASE 1 4.82 -9.3 1 .57 - 1 4.5 -0.78 -0.72
THIN 1 5 . 14 -8. 5 1 .59 - 1 3 .6 -0.63 -0.68
SUPP 1 5 .48 -9.0 1 .65 - 1 4.0 -0.75 -0.72
HGM 1 5 .90 -9.2 1 .49 -24. 1 -0.78 -0.72
Poor pasture quality (55% NDF, 1 0. 5 MJ ME/kg DM) and rolling terrain
BASE 1 6.58 + 1 .4 1 .40 -23.9 -0.29 -0.23
THIN 1 6.84 + 1 .8 1 .3 8 -24.8 -0. 1 7 -0.22
SUPP 1 6.65 -2. 1 1 .5 1 -2 1 .2 -0.26 -0.23
HGM 1 7. 24 - 1 .6 1 .43 -27. 1 -0.29 -0.23
Additional supplements: 1 kg DM supplement (35% NDF, 12 MJ ME/kg DM)
BASE 1 6.70 +2. 4 1 .88 +2.7 -0.04 +0.02
THIN 1 6.88 +2. 2 1 .89 +3. 1 +0. 08 +0.03
SUPP 1 7.36 +2. 2 1 .96 +2.5 0.00 +0.03
HGM 1 7.95 +2. 7 2.02 +3.6 -0.04 +0.02
Chapter 8 . . . . SUpp (0.55 kg milk/kg DM supplement or 4 1 g MS/kg D M supplement) cows achieved the highest and lowest response to supplements, respectively. Diminishing response to supplements at higher supplement allowances are consistent with the findings of Reis and Combs (2000). The fmding that the HGM cow achieved a greater response to supplements than the medium genetic merit cows (BASE, THIN and SUPP) is also consistent with the findings of Kennedy et al. (2002) who estimated responses to concentrates of 0.89 and 0.66 kg of milk/kg of concentrate in high and medium genetic merit animals at medium concentrate allowances.
After considering the results of the simulations, and their general agreement with experimental studies, a more in depth explanation of the model is warranted. Firstly, the MOOSIM model recognises the cow has certain requirements to maintain life and functional processes such as maintenance and pregnancy (Oldham and Emmans, 1 989). Maintenance and then fetal growth have the highest priority for energy use. Once the maintenance and fetal growth functions have been satisfied, the mammary gland acts as a pull mechanism driving feed intake, which has been a long held view (Bauman and Currie, 1 980; Knight et aI., 1 994).
In the present model, however, the extent of the feeding drive is not controlled entirely by the mammary gland. An animal's feeding drive is predicted through the use of total lactation E BV and reaction norm information, which provide an estimate of an animal's genetic potential to consume feed and then to convert this feed into milk. Use of reaction norm information accounts for inherent or evolutionary drives of speci fic genotypes in particular environments (Emmans and Kyriazakis, 200 1 ; Yearsley et aI., 200 1 ). The methodology applied in the present model provides a more robust genetic basis for milk yield potential than prior expressions such as peak milk yield potential. Future inclusion of individual differences in reaction norms, might improve the accuracy of the model. For instance, genetic variation ensures some OHF genotypes perform well in low input, pasture-based environments (Chapter 5).
A key advance of the model is it predicts what will happen rather than to account for what has already happened (Emmans and Kyriazakis, 200 1 ). For example, feed intake is not needed to predict milk yield, and milk yield is not needed to predict feed
. . . .. . . Development of MOOS I M
intake. Instead, animal genotype and feed allowance are specified and then feed intake and milk yield are predicted from these two components. This approach may then lead to differences between predicted and actual values. But, it is unrealistic to know the feed intake of a cow before it is even offered the feed. Likewise, it is highly unlikely that the potential milk yield of animal offered a specific feed is known.
The MOOSIM model has been developed for NZ production systems and cows. However, the aim is for the concepts and functions to be applied and transferred t o all production systems and cows. Conversion o f EBV t o the scales and genetic bases used in each country, reaction norm information and adaptation of feed allowance and BCS measures are needed for the international application of MOOSIM. The present study has provided reaction norm functions for overseas Holstein Friesian genotypes. However, these functions are relative to other NZ breeds of cattle. The validity of these functions compared to other breeds of cattle in different systems must be verified. Local scales of BCS can be converted to a NZ basis using the equations of Roche et al. (2004), or functions adapted to be relevant for localised scales. Similar procedures can be used for the conversion of feed allowance measures.
The nutritional components of feed have been described by the three key quality measures of ME concentration, NDF content and digestibility, each used for a specific purpose. For instance, the estimated average ME concentration of the diet is used to define efficiencies of feed use for maintenance, lactation, and growth. NDF content is used to account for physical limitations to intake (i.e. rumen fill and distension) through the initial estimate of feed intake. Digestibility is used to adjust the energy cost of grazing with higher grazing costs associated with low digestibility feeds. No additional feed quality measures were used such as protein content or macro and micro-mineral densities. The model assumes an animal's requirements for these components are met through a balanced diet. The balance should be assessed through the use of models such as CamDairy (Hulme et aI. , 1 986) and the Cornell Net Carbohydrate and Protein System model (Fox et aI., 1 992), which use feed databases describing the exact nutrient compositions of each feed.
Chapter 8 . . . .
C O N C L U S ION
The MOOSIM model represents the first attempt to include reaction norm information when predicting dairy cattle performance in a wide range of environments. Reaction norm information, in conjunction with total lactation EBV for milk, fat and protein, are used to calculate a cow's genetic potential in the specified environment. An estimated breeding value for BCS is also used to define the body fat trajectory of cow, which is also modified by c limatic and nutritional environment. The features of the model enable it to estimate feed intake
independently of milk yield, and milk yield independently of feed intake.
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