Abstracts - Canadian Science Publishing

Abstracts Proceedings of the 2008 Meeting of the Animal Science Modelling Group composition affects the conversion and losses of N at several levels in several units of the farm system. For a full, integrated evaluation of the effect of nutritional strategies on N utilization and losses at the farm level, reliable estimates of excreta production and composition are essential. Therefore, a model was developed to predict organic matter (OM), carbon (C) and N output in various faecal and urinary components as a function of intake and diet composition. An extant dynamic, mechanistic model of rumen function and subsequent nutrient supply (Dijkstra et al. 1992, 1996) was extended with static equations that describe intestinal digestion. Feed intake was estimated using the Dutch fill unit system, based on feed intake capacity of cows and satiety value of feeds. The rumen model predicts VFA production and flows of undegraded feed and microbial matter to the small intestine. Digestion of feed or microbial matter in the small intestine was represented by constant or time-independent digestion coefficients. Substrate fermentation in the hindgut was calculated from fractional rates of degradation and passage. Endogenous protein secretion was related to indigestible DM faecal flow. Potential milk production was simulated as the lowest value of calculated milk production based on either total absorbed energy, or lipogenic, glucogenic or aminogenic nutrient supply assuming a constant milk composition. Excretion of urinary N was calculated by assuming zero N retention in the body. The effects on manure composition were explored for a grass silage diet (G; 70% silage, 30% concentrate) by simulating the effect of replacement (DM basis) of the grass silage component with 50% maize silage (M), 15% straw (S), 15% beet pulp (B), or 15% potatoes (P) (Table 1). The highest C:N ratio and lowest Ninorganic:Ntotal ratio in manure was obtained with diet M, because of a large amount of undegraded fibre and a low excretion of N in urine. Diet S also has a high level of undegradable fibre. However, in comparison to diet M, the amount of simulated energy yielding nutrients absorbed from the gut was low, and hence less of the absorbed protein is used for milk protein synthesis, resulting in an increased urinary N excretion and lower N efficiency. The inclusion of low protein byproducts (diet B and P) improved N efficiency compared with diet G, but effects on level and type of OM and N excretion were rather small. In conclusion, the model predicts the efficiency of N utilization by dairy cows and the

Edited by J. France1, E. Kebreab2, and J. A. Metcalf3 1

Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1; 2Department of Animal Science, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2; and 3Nutreco Canada Agresearch, Suite 200, 150 Research Lane, Guelph, Ontario N1G 4T2, Canada. This group meets yearly for one-day meetings. The 2008 meeting was sponsored by Adisseo USA, Inc., Alpharetta, GA; Evonik Degussa GmbH, Hanau, Germany; Agri-King, Inc., Fulton, IL, USA; and Nutreco Canada, Inc., Guelph, Ontario. It was held on July 7 at The Westin Indianapolis, prior to the ASASADSA Joint Annual Meeting. Summaries of the papers presented follow. Each summary has been peer reviewed and edited for clarity. The person who presented the paper is identified with an e-mail address. Development of a model to predict the effect of nutritional strategies on manure composition of dairy cows. J. Dijkstra1, A. Bannink2, E. A. Lantinga3, J. France4, E. Kebreab5, and J. W. Reijs6. 1Animal Nutrition Group, Wageningen University, 6709 PG Wageningen, the Netherlands, [email protected]; 2Animal Sciences Group, Wageningen UR, 8219 PH Lelystad, the Netherlands; 3Biological Farming Systems Group, Wageningen University, 6709 PG Wageningen, the Netherlands; 4 Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1; 5National Centre for Livestock and Environment, Department of Animal Science, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2; 6Agricultural Economics Research Institute, Wageningen UR, 6706 KN Wageningen, the Netherlands. Excretion of faecal and urinary nitrogen (N) by dairy cows contributes to N emission to the environment. A reduction of the dietary protein content generally results in a more than proportional reduction of the urinary N excretion (Kebreab et al. 2002), but the variation in response is large. Plant utilization of N from soil-applied dairy cow slurry has been shown to be affected by nutrition of the cow (Sørensen et al. 2003). Thus, diet 725

726 CANADIAN JOURNAL OF ANIMAL SCIENCE Table 1. Effect of diet on simulated N efficiency and excretion and composition of dairy cattle manure

1

DM intake (kg DM d ) Milk N (% of feed N) Total OM excretion (kg d 1) Urine OM excretion (kg d 1) Total N excretion (g d 1) Urine N excretion (g d 1) Manure C:N ratio Ninorganic: Ntotal ratio

G

M

S

B

P

19.3 27.3 4.89 0.67 395 240 5.9 0.47

20.3 32.2 5.55 0.47 316 163 8.1 0.38

17.9 27.8 4.70 0.55 336 191 6.6 0.43

20.1 28.4 5.07 0.65 388 227 6.1 0.45

20.3 28.3 5.16 0.67 391 233 6.1 0.47

partitioning of OM and N excretion in faeces and urine. This knowledge contributes to development of nutritional strategies that reduce N losses during manure storage and improve utilization of N from manure N applied to soil. Key words: Modelling, manure composition, N efficiency, C:N ratio, dairy cattle Dijkstra, J., Neal, H. D. St. C., Beever, D. E. and France, J. 1992. Simulation of nutrient digestion, absorption and outflow in the rumen: model description. J. Nutr. 122: 22392256. Dijkstra, J., France, J., Assis, A. G., Neal, H. D. St. C., Campos, O. F. and Aroeira, L. J. M. 1996. Simulation of digestion in cattle fed sugar cane: prediction of nutrient supply for milk production with locally available supplements. J. Agric. Sci. (Camb.) 127: 247 260. Kebreab, E., France, J., Mills, J. A. N., Allison, R. and Dijkstra, J. 2002. A dynamic model of N metabolism in the lactating dairy cow and an assessment of impact of N excretion on the environment. J. Anim. Sci. 80: 248 259. Sørensen, P., Weisbjerg, M. R. and Lund, P. 2003. Dietary effects on the composition and plant utilization of nitrogen in dairy cattle manure. J. Agric. Sci. (Camb.) 141: 7991. Simultaneous modelling of growth and net energy intake in pigs. A. B. Strathe1, A. Danfær1, A. Chwalibog1, and H. Sørensen3. 1Section of Nutrition, Department of Basic Animal and Veterinary Sciences, Faculty of Life Sciences, University of Copenhagen, DK-1870, Frederiksberg, Denmark, [email protected]; 2Section of Statistics, Department of Natural Sciences, Faculty of Life Sciences, University of Copenhagen, DK-1870, Frederiksberg, Denmark. The objective of the present study is to model the pattern of energy intake and growth, and predict body energy (BE) deposition in pigs from weaning to approximately maturity. The energy intake in the model is presented as net energy (NE) because the diets used in the experiment are formulated on the basis of NE, as NE systems are currently the recommend energy evalua-

tion system for formulating diets to growing pigs (Just 1982; Noblet et al. 1994). The proposed mathematical model is derived from the definition of NE and the hypothesis that pigs NE intake (NEI) increases curvilinear to a maximum and then declines to a maintenance level as the animal matures. Thus, it is assumed that NEI is a quadratic function of the metabolic body weight (MBW), which can be parameterized in terms of maximum intake and the BW at maximum intake. The maintenance requirement is proportional to MBW. The increment in BE per unit of time represents the difference between NEI and NE maintenance. An allometric relation is used to model the relationship between BE and BW, i.e., we assume that BW A BE B : Data for estimation of the model are based on accumulated NEI (acNEI) and BW measurements conducted from weaning to approximate maturity on 13 pigs. Serial correlation is encountered in the data (acNEI and BW). Thus, the model is parameterized as a state-space model and implemented in the parameter estimation software for continuous time stochastic modelling (Kristensen and Madsen 2003). The derived parameter estimates are presented in Table 2 along with population mean, relative standard deviation, minimum and maximum parameter values for the 13 animals. The relationships between predictions by the model and data are very good for acNEI (acNEIpredicted 421.003 acNEI observed, R2 0.99) and BW (BWpredicted 2.91.01 Wobserved, R2 0.99), respectively. Based on the estimated parameters the BE content can also be calculated and compared with the observed values from slaughter measurements (BEpredicted 13210.81 Table 2. Structural parameter estimates presented as sample mean, relative standard deviation (Rel. sd), minimum and maximum of estimates in the population Parameter

Mean Rel. sd 1

Maximum intake (MJ d ) 29.8 BW at maximum 198.1 intake (kg) 0.324 Scaled energy density A (kg MJ 1) Exponent B 0.811 0.268 Maintenance requirement (MJ kg0.75)

14 29

Minimum Maximum 21.3 116.0

34.3 276.0

41

0.192

0.641

6 9

0.730 0.232

0.867 0.303

SUMMARIES OF COMMUNICATIONS

BEobserved, R2 0.61, n 13). Provided that the model is able to predict BE contents the estimated maintenance requirement 232303 kJ kg 0.75 is valid and the apparent under-estimation of the requirement compared with fasting heat production measurements can be explained by the extended range of BW (7 467 kg) in the present study compared with BW range in which maintenance requirements have usually been estimated. It is concluded that the proposed method for modelling the pattern of energy intake and growth from weaning to approximately maturity is promising because only five structural parameters are needed. BE contents and maintenance requirements can be derived from simple intake and BW measurements. Key words: Growth, net energy intake, state-space modelling Just, A. 1982. The net energy value of balanced diets for growing pigs. Livest. Prod. Sci. 8: 541 555. Kristensen, N. R. and Madsen, H. 2003. Continuous time stochastic modelling CTSM 2.3. user guide and mathematics guide. Technical University of Denmark, Lyngby, Denmark. Noblet, J., Fortune, H., Shi, X. S. and Dubois, S. 1994. Prediction of net energy value of feeds for growing pigs. J. Anim. Sci. 72: 344 354.

Separation of bacterial and protozoal pools in the UC Davis model of dairy cow nutrition and metabolism. J. R. Knapp1 and J. L. Firkins2. 1Fox Hollow Consulting, LLC, 424 W. 5th Ave., Columbus, OH 43201, USA, [email protected]; 2The Ohio State University, 223 Animal Science Bldg, 2029 Fyffe Court, Columbus, OH 43210, USA. To enable modelling of within-day variations in dietary nutrient intake and further enhance predictions of nitrogen recycling and post-ruminal nitrogen availability, the rumen microbial pool was revised as a bacteria pool and a pool of rumen protozoa was added to the UC Davis (Molly) model. In general, the approaches of modelling rumen protozoa followed that for rumen microbes of Baldwin et al. (1987) and Baldwin (1995), including chemical composition of bacteria and protozoa from Reichl and Baldwin (1975) and energy and nitrogen metabolism of Baldwin and Denham (1979). The protozoa pool is a state variable, with input being growth rate. Protozoa are able to utilize amino acids, energy from fermentation of carbohydrates, and microbial dry matter for maintenance and growth. Also, protozoa are able to ingest and store carbohydrate as polysaccharides and engulf bacteria. Bacterial engulfment was modelled as a predator-prey relationship, dependent on both populations. Protozoa have two fates: death or passage out of the reticulorumen. Protozoal passage is a function of the passage of liquid and small particles; no selective retention in the rumen is

727

specified. Initial pool sizes of bacteria and protozoa were estimated from Sylvester et al. (2005). Model behavior was tested by observing general patterns of response to varying dietary inputs, and sensitivity analyses of added equations were conducted. Bacterial and protozoal growth followed fermentable carbohydrate availability in appropriate patterns. Pool sizes of bacteria and protozoa were appropriately affected by changes in the rate constants for passage of liquid and small particle fractions. Data on microbial pool sizes and passage from Noftsger et al. (2005), Sylvester et al. (2005), and Karnati et al. (2007) were used to challenge the model. At rumen liquid volumes and dietary inputs set to the data of Sylvester et al. (2005), the model satisfactorily estimated bacterial and protozoal rumen pool sizes and protozoal passage, but underestimated bacterial passage by 25% with the two different diets. With respect to the data of Noftsger et al. (2005) and Karnati et al. (2007), the model accurately predicted protozoal passage, but underestimated the rumen pool sizes of bacteria and protozoa by 60%. These results indicated that the model can accurately predict protozoal passage and the metabolizable protein available from this source, but refinement is needed in bacterial passage associated with liquid and small particles fractions. Recent research on ruminal 15N utilization from our laboratory will be used in the next step of model development and analysis, particularly with regard to microbial pool sizes and passage rates. Key words: Rumen function, microbial passage, nitrogen availability, metabolizable protein Baldwin, R. L. 1995. Modeling ruminant digestion and metabolism. Chapman and Hall, London, UK. Baldwin, R. L. and Denham, S. C. 1979. Quantitative and dynamic aspects of nitrogen metabolism in the rumen: a modeling analysis. J. Dairy Sci. 49: 1631-1639. Baldwin, R. L., Thornley, J. H. and Beever, D. E. 1987. Metabolism of the lactating cow. II. Digestive elements of a mechanistic model. J. Dairy Res. 541: 107-131. Karnati, S. K. R., Sylvester, J. T., Noftsger, S. M., Yu, Z., St-Pierre, N. R. and Firkins, J. L. 2007. Assessment of ruminal bacterial populations and protozoal generation time in cows fed different methionine sources. J. Dairy Sci. 90: 798-809. Noftsger, S., St-Pierre, N. R. and Sylvester, J. T. 2005. Determination of rumen degradability and ruminal effects of three sources of methionine in lactating cows. J. Dairy Sci. 88: 223-237. Reichl, J. R. and Baldwin, R. L. 1975. Rumen modeling: rumen input-output balance models. J. Dairy Sci. 58: 779-890. Sylvester, J. T., Karnati, S. K. R., Yu, Z., Newbold, C. J. and Firkins, J. L. 2005. Evaluation of a real-time PCR assay quantifying the ruminal pool size and duodenal flow of protozoal nitrogen. J. Dairy Sci 88: 2083-2095.

728 CANADIAN JOURNAL OF ANIMAL SCIENCE

Evaluation of mathematical models to predict residual feed intake in growing steers. G. D. Cruz, J. W. Oltjen, and R. D. Sainz. Department of Animal Science, University of California, Davis, CA 95616 USA, [email protected]. The objective of this study was to evaluate the applicability of intake models to estimate residual feed intake (RFI). To obtain RFI data, it is necessary to measure and record daily feed intake for each animal, which is laborious and expensive. This has limited its adoption as a feed efficiency measurement. Several workers have developed methods to predict individual DMI. Two models based on growth, carcass composition and nutrient requirements [Perry and Fox (1997)-Model 1, and Guiroy et al. (2001) - Model 2] were evaluated using a dataset of 60 AngusHereford crossbred steers (296 kg initial BW). Animals were fed individually twice daily with a corn-based ration (1.68 Mcal NEm kg1, 13% CP on a DM basis) and refusals were measured daily. Model 1 and Model 2 accounted for 29 and 25% of total DMI variance, respectively. The mean square errors of prediction (MSEP) were 2.02 and 1.86 kg d 1 for Model 1 and 2, respectively (Table 3). By design, both models had a mean bias equal to zero. The slope bias for both models explained the greatest proportion of the MSEP for DMI. Considering that mean RFI values for high and low RFI groups are typically around 1 kg d1 above or below zero, respectively, neither model predicted DMI with the precision required for estimation of RFI. This study showed that although these models are accurate, they do not have the precision necessary to predict individual RFI. Key words: Model evaluation, intake models, residual feed intake, beef cattle Guiroy, P. J., Fox, D. G., Tedeschi, L. O., Baker, M. J. and Cravey, M. D. 2001. Predicting individual feed requirements of cattle fed in groups. J. Anim. Sci. 79: 1983 1995. Perry, T. C. and Fox, D. G. 1997. Predicting carcass composition and individual feed requirement in live cattle widely varying in body size. J. Anim. Sci. 75: 300 307. Table 3. Model predictions from Perry and Fox (1997) and Guiroy et al. (2001) compared with individual intake data Item MSEP MSEP decompositionz Mean bias, UM Slope bias, UR Random errors, UD UMURUD 1.00.

z

Model 1 Perry and Fox (1997)

Model 2 Guiroy et al. (2001)

2.02

1.86

0.00 0.59 0.41

0.00 0.52 0.48

Modelling rumen wall functionality and potential for a systems biology approach. A. Bannink1, J. France2, E. Kebreab3, and J. Dijkstra4. 1Department of Animal Production, Animal Sciences Group, Wageningen University Research Centre, Lelystad, PO Box 65, 6500 AB Lelystad, the Netherlands, [email protected]; 2 Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1; 3National Centre for Livestock and Environment, Department of Animal Science, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2; 4Animal Nutrition Group, Wageningen University, 6709 PG Wageningen, the Netherlands. The post-parturient high-yielding dairy cow adapts to the rapid increase in feed and concentrate intake to maintain rumen function. Besides rumination and saliva production, the rumen wall plays a key regulatory role, demonstrated by its rapid development after parturition (Dirksen et al. 1997; Bannink 2007). Bannink et al. (2008) took biopsies of the rumen wall from 2 wk before till 12 wk after parturition to study rumen wall adaptation to a fast (F, within 10 d) or slow (S; within 20 d) post-parturient increase to maximum levels of concentrate feeding of 12.5 kg DM d1. In comparison to preparturient epithelium thickness, the F treatment was associated with a 30% decrease in epithelium thickness to 75 mm during the first 3 wk, whereas with S it increased by 35% to 115 mm within 2 wk. Changes in stratum corneum thickness were of the same sign and in the order of 4 mm for both F and S. Furthermore, papillae size increased faster with F than with S. A dynamic mechanistic model was constructed, which combines the representation of the absorption of volatile fatty acids (VFA) by the rumen wall and rumen wall adaptation to feeding strategy (Bannink et al. 2008). It was hypothesized that epithelial adaptation is evoked by the integration of the history of epithelia exposure to rumen VFA load. This memory function and the goaldirected adaptation of rumen epithelial tissue were represented by a mechanism of negative feed-back control. The model represents two features of the adaptive response: 1. increase of papillae shape/size resulting in increased epithelial surface exposed to rumen contents, 2. accretion of epithelial tissue mass to increase acid processing capacity. Simulation results demonstrated that a doubling of VFA production rate from 65 to 130 mol VFA d1 within 11 or 21 d postpartum (dpp) resulted in 1 and 11% or 12 and 12% increase in rumen VFA concentrations after 10 and 20 dpp, respectively, resembling observations. Changing incremental VFA production rates to the maximum level within 20, 10, 7.5 or 5 dpp gave maximum VFA concentrations during the early time course of lactation (at 3, 3, 8 or 5 dpp, respectively) equal to 93, 95, 102 and 104% respectively of the 122 mmol L1 reached at 25 dpp. The proposed mechanism of adaptation of VFA absorption capacity to rumen VFA load indicated that


even with the fastest increment severe accumulation of VFA was prevented and that the maximum simulated effect on rumen fluid pH would remain less than 0.05 units. Simulation results further indicated that, in comparison to the effect of a reduced pH, adaptation of the rumen wall had a much larger impact on VFA clearance and consequently on the risk of rumen contents becoming too acidic. Refinement of the model remains strongly hampered by the lack of data concerning repeated and combined measurements of rumen VFA load and rumen epithelial tissue status and function. Furthermore, current observations are restricted to macroscopic and histological phenomena only. Genomics techniques may deliver valuable additional information concerning the effect of history of feeding strategy and rumen status on the genetic control and physiological limits of adaptation and function of rumen epithelial tissue. A systems biology approach will facilitate the translation of gene expressions and other -omics data into appropriate parameterizations of models of whole rumen function. Key words: Modelling, rumen epithelium, adaptation, cows Bannink, A. 2007. Modelling volatile fatty acid dynamics and rumen function in lactating cows. Ph.D. thesis, Wageningen University, Wageningen, the Netherlands. 253 pp. Bannink, A., France, J., Lo´pez, S., Gerrits, W. J. J., Kebreab, E., Tamminga, S. and Dijkstra, J. 2008. Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall. Anim. Feed Sci. Technol. doi:10.1016/j.anifeedsci.2007.05.002, (in press). Dirksen, Von G., Dori, S., Arbel, A., Schwarz, M. and Liebich, H.-G. 1997. The rumen mucosa its importance as a metabolic organ of the high producing dairy cow. Israel J. Vet. Med. 52: 7379. Selenium kinetics in green sturgeon. N. De Riu1,3, S. M. Lee2, D. F. Deng1, G. Moniello3, S. S. O. Hung1, and R. D. Sainz1. 1University of California, 1 Shields Avenue, Davis, CA 95616, USA, [email protected]; 2Kangnung National University, 123 Jibyeon-Dong, Gangneung 210-702, Korea; 3University of Sassari, via Vienna 2, 07100 Sassari, Italy.

sampled at 0, 1.5, 3, 6, 12, 24 and 48 h and analyzed for total Se. The Se kinetic data were subjected to compartmental analysis. The pools included in the model are: the gastrointestinal (GI) tract; extracellular fluid (ECF, which includes blood), and separated into free and protein-bound Se; and body tissues. Excretory processes (fecal and urinary) are also included. Most of the equations are first-order, so that flow from one compartment to the other is proportional to the originating pool size. Absorption was modelled using a Michaelis-Menten type equation. Urinary excretion was modelled using a threshold model, with the rate being proportional to the free circulating Se pool size but only above the threshold re-absorption level (0.05 mg mL 1). The half-maximal content of Se in the GI tract (Km10) was set at 100 mg; this value was determined by trial and error to produce acceptable model behaviour. Although not fixed beforehand, the fitted parameter values produced assimilation efficiencies of 100% in all cases. Presumably this was due to the free amino acid form of the SeMet; digestion of natural proteins would likely be lower. Complete assimilation was also supported by nearly identical blood Se kinetics curves for oral or intravenous administration. Although the excretion and tissue transfer rates were not well defined, the model behaviour was substantially worse when these rates were not included, indicating that, in fact, these processes are important determinants of Se kinetics in the circulating pools. Therefore, the model is over-parameterized, but analysis of model behaviour indicates that the pools shown in Fig. 1 are required to obtain reasonable estimates. Future experiments should include data collected from those pools. These results must indicate that at 48 h post-administration, more than 20% of the Se dose may be retained in body tissues. These predictions will require experimental confirmation, and longer sampling times are recommended. Key words: Selenium, kinetics assimilation, sturgeon

Dose (IB)

GI tract (0)

Feces

k10

Dose (IV) k31

k41

Bio-accumulation of selenium to toxic levels is one result of estuarine pollution with this element. In order to determine the kinetics of selenomethionine (SeMet) assimilation in green sturgeon (Acipenser medirostris), 40 fish (2 4 kg, 2 yr old) were fitted with an esophageal intubation tube and a dorsal aorta cannula and received 0 (sham), 250, 500, or 1000 mg Se kg1 body weight through either the feeding tube (starch solution) or the dorsal aorta cannula (saline solution). Blood was

729

Body tissues (3)

ECF free (1)

Urine (4)

k13 k21

k12

ECF proteinbound (2)

Fig. 1. Compartmental model of SeMet kinetics.


Use of meta-analysis to build a mechanistic model of responses of ruminal digestion to dietary fibre in cattle. D. Sauvant1 and D. R. Mertens2. 1INRA-AgroParisTech, Paris, France, [email protected]; 2US Dairy Forage Research Center, Madison, WI 53706, USA. The underlying structures of published rumen mechanistic models are fairly similar, but they often lack of accuracy when compared with data reported in the literature (Offner and Sauvant 2003). Our objective was to develop a simple mechanistic model of the rumen, in which the major parameters are adjusted based on empirical equations obtained through meta-analyses of two databases of published experiments. Equations were generated from databases on growing or lactating cattle that involved experiments (Exp) focused on the influences of (1) level of dry matter intake [63 Exp, 146 treatments (Trt), DMI 2.0290.84% of live weight] or (2) dietary NDF or concentrate proportion in the ration (219 Exp, 552 Trt, NDF 38.5912.7%DM). Equations were derived using both across- and intra-experiment relationships. The rumen model comprised six compartments with their main in- and out-flows: digestible and undigestible NDF (dNDF, uNDF), non-microbial cell contents (CC), water (H2O), microbial DM (MIC), and volatile fatty acids (VFA). All values were expressed as percentage of live weight. Major model parameters that were adjusted empirically using dietary DMI and NDF relationships in the two databases included: daily mastication, rumen contents and duodenal flows of dNDF, uNDF, CC, H2O, MIC and VFA. The potential digestibility of NDF was based on actual whole tract NDF digestibility. For the major relationships (e.g., duodenal liquid flow as a function of NDF intake), consistency across the two databases was checked using meta-analyses. The adjusted parameters, obtained with the Modelmaker program, were salivary secretion rates during mastication (274 mL min 1) and rest (16 mL min 1), fractional degradation rate of dNDF (max 8.8% h 1) and CC (16.5% h1), fractional absorption rates through rumen wall of H2O (10.8% h 1) and VFA (25.0% h1). The fractional outflow rates (ko,% h1) of liquid and NDF compartments were functions of DMI%LW (e.g. koliq 8.441.39 DMI%LW, kouNDF 1.00 kodNDF 4.821.98 DMI%LW, 0.18 DMI%LW). Model responses to variations in DMI and NDF were evaluated. Simulations of influences of these factors and of their interactions resulted in acceptable intra-experiment prediction accuracy. For instance the regression between measured (Ym 0.4990.20 %LW) and simulated (Ys) duodenal flows of NDF was: Ym 0.98Ys 0.02 (R2 0.97 and RMSE 0.05). Key words: Cattle, dietary fibre, meta-analysis, rumen models

Offner, A. and Sauvant, D. 2004. Comparative evaluation of the Molly, CNCPS and LES rumen models. Anim. Feed Sci. Technol. 112: 103 130. Ruminal starch, fiber, and protein digestion parameter estimates for Molly. J. A. D. R. N. Appuhamy and M. D. Hanigan. Department of Dairy Science, Virginia Tech, Blacksburg, VA 24061, USA, [email protected]. The model developed by Baldwin et al. (1987), known as Molly, predicts nutrient degradation and microbial metabolism in the rumen based on nutrient inputs to the animal. Molly was previously observed to provide biased predictions of ruminal nutrient digestion (Hanigan et al. 2006), indicating the model requires reparameterization. The objective of the present work was to derive parameter estimates for starch, fiber, and protein digestion in the rumen while accommodating experimental bias (EB) in nutrient inputs and duodenal nutrient flows. The version of Molly described by Hanigan et al. (2006) was used along with the data described in that same publication. Input data included the nutrient contents (%) of feed (fNut,Fd) such as starch, crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF), and the corresponding ruminally undegradable fractions (fRuNut,Fd) (g/g). Observed outputs included duodenal flows of each nutrient (FNut,SI) (kg d1). Where nutrient contents of ingredients were unavailable, tabular values from NRC were used. As this may introduce input error, mean EB for model input was assessed within an experiment and used to correct calculated inputs. Ruminal degradation rate constants (kd) for starch, CP, and fiber degradation in the rumen were calculated from fNut,Fd and fRuNut,Fd: kd

24 8

ln(fRuNut;Fd =fNut;Fd )Adjk

where Adjk is a scalar (set initially to 0) that can be used to adjust the NRC (2001) derived kd to achieve appropriate predictions of ruminal degradation of each nutrient as determined by duodenal appearance of the nutrient. The kd for hemicellulose (kd(Hc)) was calculated from the kd for cellulose (kd(Ce)): kd(Hc) FHcCS1 kd(Ce) The EB in FNut,SI was accommodated during fitting by inclusion of an additional set of bias vectors (BiasNutP(i)) where each element (i) pertained to one experiment: FNut;SI(Unbias) FNut;SI BiasNutP(i) BiasNutP(i) were initially set to 0 and included in the adjustable parameter set along with Adjk and derived by optimization. As the parameter set was very large, independent optimization runs were carried out for


each nutrient using a maximized log likelihood approach and the Nelder-Mead optimization algorithm (ACSL Optimize, Version 11.8). The final parameter estimates (d 1) of Adjk for starch, insoluble protein, cellulose, and kHcCs1 were 2.5290.03, 12.2490.00, 90.890.02, and 0.6690.00 respectively as compared to the estimates when EB set to 0; 3.02, 3.30, 59.8, and 0.43, respectively. A summary of prediction errors of FNut,SI is presented in Table 4. The mean BiasNutP(i) estimates for starch, CP, ADF and NDF were 0.129 0.04, 0.0890.06, 0.0490.60, and 0.0590.90 kg d 1 respectively. Experimental bias was found to be a significant contributor to observed variation in duodenal flow. Accommodation of EB during parameter estimation was achieved within ACSL which should yield better parameter estimates. Key words: Experimental bias, Molly, nutrients, ruminal digestion Baldwin, R. L., Thornley, J. H. M. and Beever, D. E. 1987. Metabolism of the lactating cow: II. Digestive elements of a mechanistic model. J. Dairy Res. 54: 107 131. Hanigan, M.D., Bateman, H. G., Fadel, J. G. and McNamara, J. P. 2006. Metabolic models of ruminant metabolism: recent improvements. J. Dairy Sci. 89 (E. Suppl.): E52 E64. Cortisol surge is associated with differences in fractional protein synthesis rates comparatively measured by the flooding dose with L-[ring-2H5]phenylalanine administered intraperitoneally or intravenously in piglets. K. Bregendahl1, X. Yang1, L. Liu1, J. T. Yen2, T. C. Rideout1, Y. Shen1, G. Werchola1, and M. Z. Fan1. 1Centre for Nutrition Modelling, Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario, Canada N1G 2W1, [email protected]; 2USDA/ARS, US Meat Animal Research Center, Clay Center, NE 68933, USA. Fractional protein synthesis rates (FSR) are widely measured by the flooding dose technique via either the intra-peritoneal (ip) or the iv route for tracer delivery (Bregendahl et al. 2004). This study was carried out to

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systematically compare differences in tracer incorporation patterns and FSR in organs and tissues measured by the flooding dose via ip versus iv route using L[ring-2H5]Phe in piglets. Five blocks of two littermate gilts were weaned at 16 d of age, fed a commercial starter diet, and surgically implanted with catheters in the jugular vein and carotid artery. The piglets were randomly assigned to receive a flooding dose of Phe (1.5 mmol kg1 BW, 40 molar%-enriched with [2H5]Phe) in saline administered via either the ip or iv route. Molar enrichments of [2H5]Phe in the free and bound pools were measured by gas chromatographymass spectrometry and calculated by calibration curves (Fan et al. 2006). For the ip administered pigs, molar enrichment of [2H5]Phe in plasma (y) increased logarithmically {y 24.60[1e(0.403x)], R2 0.99; PB0.05, n 30} to 25 molar% with time (x) between 0 and 30 min of tracer injections and reached 99% of the maximal enrichment at 11 min post-injection. By contrast, molar [2H5]Phe enrichment in plasma of the iv group rose to its peak level within 3 min of tracer injections and then decreased linearly (P B0.05) with (y26.90 0.01x, R2 0.54, P B0.05, n22). The free-pool tracer enrichments observed in target organs and tissues were within the ranges of the values measured for the plasma free pools (25 27 molar%), reaching the flooding status. Administration of the tracer via the ip and iv routes induced a logarithmical pattern (P B0.05) of a surge in cortisol levels (y, nmo L1) with time (x) in plasma within 30 min {i.p. group: y278.35 115.06e(0.251x), n30, R2 0.92; vs. i.v. group: y 197.57 42.87e(0.877x), n 24, R2 0.97}. Furthermore, FSR measured in plasma, cardiac and skeletal muscles, spleen and stomach were lower (P B0.05) in the ip than in the iv group, and for plasma and skeletal muscles, this was due to the adverse effect of cortisol surge being more dramatic in the ip than in the iv group (P B0.05) at 30 min post-tracer administrations. We conclude that FSR can be measured by the flooding dose through the iv or ip route for tracer delivery. The ip route may underestimate FSR by the flooding dose for plasma, muscles and some viscera such as spleen or stomach and this concern may be addressed by a fast regime of tissue sampling to be completed within 12 to 20 min after the tracer injection.

Table 4. Mean square prediction errors (MSPE) (kg2 d 1) with and without adjustment for experimental bias (EB) and mean observed values (MObs) (kg d 1) for FNut,SI Variable

nz

MSPE (without EB)

MSPE (with EB)

% reduction

MObs

FADF,SI FNDF,SI FST,SI FyNMN,SI

31 40 23 63

0.4141 1.2245 1.2475 0.0130

0.1552 0.2855 0.5171 0.0010

62.53 76.69 58.55 92.67

2.32 3.71 2.93 0.22

z

Number of experiments. Non microbial nitrogen flow to small intestine.

y


matter. Animals can be added to the pasture and removed for slaughter. It was found that the trees should not have a negative impact on the growth of the pasture until the row spacing drops below 17 m, at which point the radiation transmitted to the pasture becomes greatly reduced. The radiation use efficiency of the pasture increases with the density of the trees to a certain degree (around 9 m tree-row spacing) and then decreases, indicating that the pasture can partially compensate for the decreased radiation. The model demonstrates that the shading of the trees begins to affect the growth of the pasture at around 17 m and becomes critically limiting at 9 m. The trees in the pasture system may also cause the cattle to be more efficient in terms of growth due to shading, which decreases heat stress. Thus, the trees decrease pasture growth, but at a certain row-spacing the higher efficiency of the cattle can compensate for the decrease in forage availability. Currently, the animal component is being improved by adding feed efficiency, animal grazing and animal growth equations. Future work will include adding soil and water components and challenging the SPS model against independent data sets.

Key words: Fractional protein synthesis rates, routes of tracer delivery, stable isotope tracers, cortisol effect, weanling pigs Bregendahl, K., Liu, L., Fan, M. Z., Cant, J. P., Bayley, H. S., McBride, B. W., Milligan, L. P. and Yen J. T. 2004. Fractional protein synthesis rates are measured by an intra-peritoneal injection of a flooding dose of 2 L-[ring- H5]phenylalanine in pigs. J. Nutr. 134: 2722 2728. Fan, M. Z., Chiba, L. I., Matzat, P. D., Yang, X., Yin, Y. L., Mine, Y. and Stein, H. 2006. Measuring synthesis rates of nitrogen-containing polymers by using stable isotope tracers. J. Anim. Sci. 84 (E. Suppl.): E79 E93. Simulation modeling of silvo-pastoral production systems. V. A. de Leoń1, T. C. Foin1, M. S. Bernardes2, and R. D. Sainz1. 1Department of Animal Science, University of California, Davis, CA, 95616, USA, [email protected]; 2Department of Crop Science, Universidade de Saõ Paulo, ESALQ, Caixa Postal 9, CEP: 13418-900, Piracicaba, Saõ Paulo, Brazil. Silvo-pastoral production systems (SPS) combine trees and pasture with livestock production systems (Gold et al. 2000). As an aid and guide to research (Thornley and France 2007) a mathematical model was constructed to study the interactions between tree, pasture and animal components of a SPS, specifically the effects of radiation on pasture and animal growth. In this model (Fig. 2), rubber trees (Seringueira RRIM) were used for the tree component, palisade grass (Brachiaria brizantha) for the pasture component and Nellore cattle (Bos indicus) for the animal component. The tree and pasture growth are driven by environmental factors including solar radiation and temperature. The trees and pasture can be consumed by the cattle and can also senesce to the dead material pool, which decays to the soil organic

Key words: Beef cattle, silvo-pasture, tropics, mathematical model, sustainable, rubber trees Gold, M. A., Rietveld, W. J., Garrett, H. E. and Fisher, R. F. 2000. Agroforestry nomenclature, concepts, and practices for the USA. Pages 378 in H. E. Garrett, W. J. Rietveld, and R. F. Fisher, eds. North American agroforestry: an integrated science and practice. ASA, Madison, WI. Thornley, J. H. M. and France, J. 2007. Mathematical models in agriculture. Revised 2nd ed. xvii906 pp. CAB International, Wallingford, UK.

Environmental Factors Temperature Radiation

Add Anim Trees

Grazing T

Efficiency

Growth T Senes T Soil OM

Animal Bact Ferm

Pasture

Waste

Senes P

Growth P Grazing P

Fig. 2. An overview of the model of silvo-pasture.

Slaughter


A stochastic renewal reward model of forage sampling and total quality costs. N. R. St-Pierre. The Ohio State University, Columbus, OH 43210, USA, [email protected]. In dairy production, there are many processes that must be monitored to achieve maximum economic efficiency. Feed production is a process that requires monitoring and intervention. Ingredient variation can be accounted for during diet formulation in instances when the distribution of nutrients within feedstuffs is known (StPierre and Harvey 1986). Alternatively, statistical process control methods can be implemented to monitor the composition of feeds subject to unpredictable and sudden threshold changes in composition, such as in forage. The objective of this research was to derive a model suitable to optimize the on-farm sampling design of forages. Poisson and renewal processes are characterized by memory-less and non-self recovery attributes, two characteristics that are reasonable for stored forages. A quality cycle is defined as the time between the start of successive in-control periods; that is, forage composition is that used to formulate the diet. The objective is to minimize the total quality cost per unit of time (C), hence requiring the calculation of the total quality costs per cycle (CCL) and the cycle length (TCL). Given that we assume a memory-less process, the incontrol period is distributed as a negative exponential with mean 1/l, and the average time for occurrence of threshold change is Tb 1/l. The expected time between the last sample while in-control and the abrupt change is t (1 (1 lh)e lh)/(l(1 elh), where hsampling interval, and e the exponential function. Thus, the expected time between the abrupt change and the next sampling time is Tc ht. We define E as the time to sample, analyze and chart the results of one sampling time. The expected time until an out-of-control signal occurs is Te h(ARL1 1), where ARL1 the average run length when the process has shifted to an out-of control state. ARL1 1/(1 b), where b P(in-control signal j process is out-of-control), i.e., the type II error. Under normality, b F(L ^ân)F(L ^ân), where F is the cumulative distribution function for the standard normal distribution, and ^ is the extent of the change in composition. We define T1 as the expected time to investigate the cause of the change, and T2 as the expected time to reformulate and implement new diets. Then TCL TbTcETeT1T2. The expected cost per cycle due to reduced production is Ca (^M0PmNc/l)(^M1PmNc)(E thARL1T1T2), where ^M0 milk production loss due to white noise in forage composition when the process is in-control (kg cow 1 d1), Pm price of milk ($ kg1), Nc number of cows, and ^M1 milk production loss due to an abrupt change in forage composition (kg cow 1 d1). Let Y be the cost per false alarm and W be the cost to reformulate and implement new diets. Then the expected cost for false alarms and for identifying the cause of the

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change and reformulating diets is Cb (sY)/ARL0W, where s is the number of samples taken while in-control: s elh/(1 elh), ARL0 is the average run length while in-control; ARL0 1/a and a P(out-of-control signal j process is in-control), i.e. the type I error. Under normality, a 2F(L). The cost per cycle for sampling and analyses is Cd (abn) ((1/l) tEhARL1 T1T2))/h, where athe fixed costs per sampling time and b is the cost per unit sampled. CCL CaCbCd, and C CCL/TCL. C is optimized with respect to h, n, and L using a genetic algorithm. A global nonlinear sensitivity analysis using extended Fourier amplitude sensitivity (FAST) showed that of the 13 inputs to the model, l, Nc, and ^M1, and Pm accounted for 93.4% of the total variance in C. Robustness assessment showed the model to be very robust to departure from normality, presence of outliers and a skewed distribution of measurements. Gradual changes in forage composition are readily detected by the optimum X-bar control chart. The model can be applied to optimize forage sampling on dairy farms with expected savings ranging between $80 and $100 cow 1 yr 1. Key words: Forage sampling, forage composition, stochastic model, control charts, sensitivity analysis, robustness St-Pierre, N. R. and Harvey, W. R. 1986. Incorporation of uncertainty in composition of feeds into least-cost ration models. 2. Joint chance-constrained programming. J. Dairy Sci. 69: 3063 3074. Prediction of rumen residence time using markers or rumen evacuation and slaughter data. P. Huhtanen1 and S. Ahvenja¨rvi2. 1Department of Animal Science, Cornell University, Ithaca, NY 14853, USA, [email protected]; 2Animal Production Research, MTT-Agrifood Research Finland, FI-31600 Jokioinen, Finland. The performance of dynamic mechanistic feed evaluation models is sensitive to parameters describing the indigestible and potentially digestible entities and the digestion and passage kinetics of digestible fractions. Passage rate (kp) is an important parameter of dynamic mechanistic feed evaluation models in addition to indigestible fraction and digestion rate of potentially digestible fractions. Accurate estimates of kp are needed to predict ruminal protein degradability or NDF digestibility accurately. However, the attempts to estimate kp based on animal and feed characteristics have not been very successful. In addition to true variation in kp, the poor performance of empirical models can result from differences between markers, kinetic models, and biological interpretation of the data. Our objective was to interpret the marker kinetics data using data from slaughter, rumen evacuation and marker kinetic studies. Ruminal passage rate of feed particles has usually been estimated from the descending phase of faecal


marker excretion curve. Comparison (n 40) of duodenal (D) and faecal sampling (F) showed that the mean compartmental residence time (CMRT2 1/kp) estimated from the descending marker curve was similar (23.0 and 22.9 h), and that they were strongly and positively correlated (R2 0.78). However, CMRT2 estimated from the descending phase of faecal curve was clearly lower than the total residence time (CMRTD 1/kr1/kp; kr flow rate from large to small particle pool) in the pre-duodenal mixing compartments (22.9 vs. 36.9 h; P B0.001). The difference between F and D sampling in the total CMRT estimated from the marker excretion curves was small (39.3 vs. 36.9 h), although significant (P B0.001). The small difference suggested that most (94%) of the CMRT was pre-duodenal. There was a close relationship between CMRT estimated from F and D sampling: CMRTD (h) 0.7(91.23)0.96(90.029) CMRTF (R2 0.964; S.E. 2.6 h). CMRTD was also closely related to the total MRT (TMRT 1/kr1/kptransit time) estimated from the faecal sampling: CMRTD (h) 9.4(92.15)0.95(90.040) TMRTF (R2 0.942; S.E. 3.5 h). The proportion of faecal CMRT2 was on average 59, 61 and 49% of CMRTF, CMRTD, and TMRTF, respectively. Rumen evacuation and slaughter techniques are alternative methods of estimating MRT in the different segments of digestive tract. Turnover or mean residence time in a segment can be estimated by dividing the pool size of indigestible component, e.g., iNDF, in different segments of the tract by the rate of iNDF intake. The proportion of reticulorumen of the total MRT was 7475% in three slaughter studies, and that of forestomachs including omasum 78-85%. These values are markedly higher than the proportion (49%) of CMRT2 of TMRTF derived from the marker data. However, the proportions of CMRTD and CMRTF of the TMRT based on the marker data (72 and 77%, respectively) are consistent with the slaughter data. The mean rumen turnover time of iNDF (47.4 h, n 63) estimated by rumen evacuation technique was about two times higher compared with CMRT2 estimated from faecal marker curve, but rather similar to pre-duodenal total CMRT estimated using labelled forages (41.3 h, n32). The CMRT2 of forage particles was 37% shorter than the total CMRT, i.e. the time feed is subjected to ruminal degradation, estimated from duodenal sampling with the latter values being consistent with rumen evacuation or slaughter data. In can be concluded that the estimated kp values are often too high in the current feed evaluation models, which lead to over-estimation of the contribution of rumen undegraded protein to MP supply and underestimation of NDF digestibility. Key words: Passage rate, rumen residence time, markers, slaughter technique, rumen evacuation

Altering the representation of hormones and adding consideration of gestational metabolism in Molly improved predictions of body fat change during lactation. M. D. Hanigan1 and C. C. Palliser2. 1Department of Dairy Science, Virginia Tech, Blacksburg, VA 24061, USA, [email protected]; 2DairyNZ, LTD, Hamilton, New Zealand. The model of Baldwin (1995; referred to as Molly) predicts various aspects of digestion and metabolism in lactating cows including nutrient partitioning between milk and body stores. However, prediction bias has been observed for body weight (BW) and body condition score (BCS) when diets of differing energy density are simulated over long periods of time. Generally, the model under-predicts weight loss in early lactation and gain in late lactation. The magnitude of the bias was reduced when a better representation of milk synthesis capacity was introduced into the model (Hanigan et al. 2007). It was hypothesized that a better representation of the effects of energy status on anabolic and catabolic hormones and a more complete representation of metabolic demands and growth associated with pregnancy would help in improving predictions of mobilization and storage. To address this hypothesis, glucose effects on each of the 3 anabolic (Anab) and catabolic (Catab) hormones were changed from a function of the CGl/iCGl (or the inverse for catabolic hormones) with a common exponent for sensitivity adjustments to the following: HAnab1 (CGl =KAnab1 )Theta1

(1)

HAnab2 (CGl =KAnab2 )Theta2

(2)

HCatab1 (KCatab1 =CGl )Theta3

(3)

where CGl represents the concentration of glucose in blood, iCGl represents the initial CGl, Hxxx represents the anabolic or catabolic hormones, Kxxx represents the reference point for CGl for each hormone, Theta represents the sensitivity exponent for each hormone. These changes allowed the reference points and sensitivity coefficients to be derived independently for each hormone class, which would alter their effects on BW and BCS gain and loss throughout lactation. This allowed each hormone to tip from a value greater than 1 to one less than 1 at different CGl and to fit iCGl to observed glucose concentrations without altering hormonal signalling. Equations were also introduced to describe weight gain and metabolite use associated with pregnancy based on the model of Koong et al. (1975), which was fitted to the observations of Bell et al. (1995). After introduction of the changes, Molly was refitted to the same extended lactation data set as previously used by Hanigan et al. (2007) for fits of the prior model version. This allowed a direct comparison of model


accuracy and precision relative to the previous version. Changes in the representation of hormones improved overall model performance mostly through reductions in prediction errors for blood glucose concentration and BCS (Table 5). In both cases slope bias associated with the predictions was reduced indicating that the changes were beneficial. The addition of enhanced pregnancy calculations did not provide additional benefit over the changes in hormones relative to model precision. However, the data used for the assessments did not include late pregnancy observations (observations were not collected after 220 d of gestation) where BW gain and metabolic demand associated with pregnancy would be expected to be significant. No significant changes in milk yield and compositon predictions were observed. Prediction errors for blood fatty acids and the rate of BCS loss in early lactation are still apparently inappropriately predicted and require further work. Key words: Model, lactation, dairy cow, milk composition, body weight Baldwin, R. L. 1995. Modeling ruminant digestion and metabolism. Chapman and Hall, London, UK. Bell, A. W., Slepetis, R. and Ehrhardt, R. A. 1995. Growth and accretion of energy and protein in the gravid uterus during late pregnancy in Holstein cows. J. Dairy Sci. 78: 19541961. Hanigan, M. D., Rius, A. G., Kolver, E. S. and Palliser, C. C. 2007. A redefinition of the representation of mammary cells and enzyme activities in a lactating dairy cow model. J.Dairy Sci. 90: 3816 3830. Koong, L. J., Garrett, W. N. and Rattray, P. V. 1975. A description of the dynamics of fetal growth in sheep. J. Anim. Sci. 41: 1065 1068.

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A new tool to predict beef cattle fatness in the field. V. H. Oddy1,2, R. C. Dobos1,3, M. J. McPhee1,3, W. McKiernan1,3, J. W. Oltjen4, and R. D. Sainz4. 1Cooperative Research Centre for Beef Genetic Technologies, Armidale, NSW 2350, Australia; 2University of New England, Armidale, NSW, Australia; 3NSW Department of Primary Industries, Australia; 4University of California, Davis, USA, [email protected]. The Australian Cooperative Research Centre (CRC) for Beef Genetic Technologies has developed a decision-aid software tool to predict beef cattle fatness in the field. The aim of the tool is to assist beef producers manage cattle to meet stringent market specifications that are related to both weight and fatness. The underlying multiple linear regression equation to predict P8 (rump) fat thickness (mm) was developed (M. J. McPhee, unpublished data) using a large matrix of inputs and outputs that have been simulated using the Davis Growth Model (DGM). The DGM is a biological model built on the dynamic steer growth model of Oltjen et al. (1986) that includes four fat deposition sub-models (Sainz and Hastings 2000; McPhee 2006). In brief, total body fat (kg) in the DGM is partitioned into four fat pools: intermuscular, intramuscular, subcutaneous, and visceral. Subcutaneous fat is then converted into 12th rib fat (McPhee et al. 2008) and subsequently converted into P8 fat (B. J. Walmsley, unpublished data). The new simple-to-use tool has been developed for the specific prediction of P8 fat depth when given various growth scenarios. The biology underlying the tool accounts for the effects of nutrition (quality and amount of feed eaten) on cattle growth and body composition as affected by variation in frame size, condition score,

Table 5. Root mean square prediction errors for Molly before and after alteration of endocrine and pregnancy representations (Molly 2008). Root mean square prediction errors (RMSPE) are expressed as a percentage of the observed mean. Mean and slope bias are expressed as a percentage of the mean square prediction error (MSPE) Molly 2007z

Molly 2008y (hormones)

Molly 2008x (pregnancy)

BW BCS Blood glucose concentration Blood NEFA concentration

5.7 21.7 12.4 87.4

5.6 15.2 7.9 85.1

5.4 15.1 7.7 85.6

Mean bias (% of MSPE) BW BCS Blood glucose concentration Blood NEFA concentration

0.1 39.0 0.3 0.1

2.5 5.4 0.1 2.0

2.2 5.0 0.1 1.2

Slope bias (% of MSPE) BW BCS Blood glucose concentration Blood NEFA concentration

3.9 17.3 65.7 6.1

11.1 5.5 17.1 3.2

9.6 3.8 14.2 2.8

RMSPE (% of observed mean)

z

With alterations as described by Hanigan et al. (2007). Molly2008 with a revised representation of HAnab1, HAnab2, and HCatab1 and after fitting to an extended lactation data set. x Molly2008 with a revised representation of HAnab1, HAnab2, HCatab1, and pregnancy after fitting to an extended lactation data set. y


sex, amount of activity and implant status. The current prototype model works for Bos taurus steers. The initial testing in the field showed some problems with over predicting P8 fat thickness and this has been rectified. The inputs to the fat calculator include initial liveweight (kg), frame size, current P8 fat depth (mm), expected average daily gain (kg d1), feed type (grain or pasture), length of time on feed (d), implant status (Yes/No) and an assessment of activity (feedyard, small or large paddock). This paper reports on two commercial data sets: data set 1 (n34): Brangus steers and grass finished, and data set 2 (n 42): predominantly Angus Santa Gertrudis steers and grain finished. The activity level for data set 1 was set as feedyard and data set 2 was set as a large paddock. To evaluate the data sets the mean bias, mean square error of prediction (MSEP) and the decomposition of the MSEP: bias, slope, and random component are reported (Table 6). The results indicate that both data sets predict carcass P8 fat depth with a reasonable degree of accuracy; no differences were detected between the observed and predicted values; the majority of MSEP decomposition was attributed to the random component and data set 2 had a larger MSEP than data set 1. Further testing on a broader sample of cattle types is recommended to test the accuracy and robustness of the model. Key words: Beef cattle, growth models, software McPhee, M. J. 2006. Modeling fat deposition and distribution in beef cattle. Ph.D. thesis, University of California, Davis, CA. 175 pp. McPhee, M. J., Oltjen, J. W., Fadel, J. G., Perry, D. and Sainz, R. D. 2008. Development and evaluation of empirical equations to interconvert between twelfth-rib fat and kidney, pelvic, and heart fat weights and to predict initial conditions of fat deposition models for beef cattle. J. Anim. Sci. (in press). Oltjen, J. W., Bywater, A. C., Baldwin, R. L. and Garrett, W. N. 1986. Development of a dynamic model of beef cattle growth and composition. J. Anim. Sci. 62: 86-97. Table 6. Assessment of the difference between observed and predicted values Item

Set 1

Set 2

n Mean observed (mm) Mean predicted (mm) Mean biasz (mm) MSEPy (mm) Bias (%) Slope (%) Random (%)

34 10.85 11.33 0.48 10.45 2.19 17.53 80.28

42 13.81 13.07 0.74 22.16 2.46 7.91 89.63

z

P 0.40 for Set 1 and P0.32 for Set 2. MSEP: mean square error of prediction.

y

Sainz, R. D. and Hasting, E. 2000. Simulation of the development of adipose tissue in beef cattle. Pages 175182 in J. P. McNamara, J. France, and D. Beever eds. Modelling nutrient utilization in farm animals. CABI Publishing, Wallingford, UK. Analysis of individual Canadian Holstein dairy cow lactation curves using standard growth functions. N. E. Odongo1, S. Lopez2, R. McBride1, E. Kebreab3, J. Dijkstra4, M. H. Fathi Nasri5, O. AlZahal1, B. W. McBride1, and J. France1. 1University of Guelph, Guelph, Ontario, Canada N1G 2W1, [email protected]; 2Universidad de Leoń, 24071 Leoń, Spain; 3 University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2; 4Wageningen University, PO Box 338, 6700 AH Wageningen, the Netherlands; 5Ferdowsi University of Mashhad, Mashhad, Iran 91775-1163. Six standard growth functions (monomolecular, Schumacher, Gompertz, logistic, Richards and Morgan) were fitted to cumulative milk yield curves obtained from 96 000 daily milk records of 319 lactations (124 first, 101 second, and 94 third parity) from Holstein dairy cows. Model goodness-of-fit was assessed using the Akaike Information Criterion. According to this criterion, the best fitting was observed with the Richards equation in 244 curves, followed by Morgan and Gompertz. Daily milk yields at different days in milk (2, 70, 140, 210 and 280 d after parturition) were estimated with each function and then compared with observed values using the concordance or reproducibility coefficient. In general, the best concordance between estimated and observed daily milk yields was observed with the Richards equation. Additionally, in first and third parity, the Richards equation was the most accurate in predicting peak yield, ymax, underestimating ymax by B1%. In first parity, this was followed by the Gompertz equation, which over-estimated ymax by B1% and in third parity by the Morgan, which over-estimated ymax by 1.2%. The observed ymax averaged 31.190.02, 40.590.04 and 41.590.05 kg d1 in first, second and third parity, respectively. There were large differences among models in the estimated value of days to peak yield because within a lactation, there were several days when milk yield was at the peak. It would therefore be difficult to state which function provided a better approximation of this lactation trait, given the difficulty to establish which the actual observed day of peak yield was. The persistency of lactation, p, ranged from 0.31 (logistic) to 0.59% per day (monomolecular) for first parity cows, from 0.29 (logistic) to 0.56% per day (monomolecular) for second parity cows and from 0.29 (logistic) to 0.56% per day (monomolecular) in third parity cows. Total milk yield at day-305 (y305) ranged from 7655 (Schumacher) to 9484 kg (logistic) for first parity cows, from 9064 (Schumacher) to 10861 kg (logistic) for second parity cows and from 9251 (Schumacher) to 10 638 kg (logistic) for third parity cows. In


first parity, the monomolecular was the most accurate predictor of y305, over-estimating it by B1%, whereas in third parity, the Richards equation was the most accurate, under-estimating y305 by B1% with the Morgan in second place, under-estimating y305 by 2.0%. These results suggest that the Richards equation

737

can adequately describe the peak yield, persistency of lactation and total milk yield at day-305 of Holstein dairy cows evaluated in this study. Key words: Lactation curve, growth function, cumulative milk yield, dairy cow