The model allowed integration of actual grazing behaviour and diurnal variation in pasture nutrient content to understand and predict differences in MP between grazing treatments. The integration between grazing behaviour and sward characteristics can generate opportunities to achieve greater efficiency in the use of nutrients throughout a grazing dairy syste

CHILIBROSTE, P.

DIJKSTRA, J.

ELIZONDO, F.

GENRO, T. C. M.

GERRITS, W. J. J.

GILES, P. Y.

MATTIAUDA, D. A.

MENEGAZZI, G.

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RMSPE of 8.9 and 5.8 % for the NRC and Gruber model, respectively. Among others, the lower proportion of forage (58%) in the diet mayexplain the observed discrepancy (see Table 2).Conclusion and implicationsThe relative elderly model of Agroscope solely including parity, energy corrected milk yield and DIM was most suitable with the recentAgroscope indoor DMI database. The relative high ER is indicative that an update of the coefficients in that model is required.ReferencesAgroscope, 2021. Apports alimentaires recommandés pour les ruminants (Livre vert). Accès: www.agroscope.ch/livre-vert (Consulted 2021).Bibby, J., Toutenburg, H., 1977. Prediction and Improved Estimation in Linear Models. J. Wiley & Sons, Chichester-New York-Brisbane-Toronto.CSIRO, 2007. Nutrient requirements of domesticated ruminants. CSIRO Publishing, Collingwood, Australia.FEDNA, 2009. Normas FEDNA. ed, Fundación Española para el Desarrollo de la Nutrición Animal, Madrid, Spain. (Consulted 2021).Gruber, L., Schwarz, F.J., Erdin, D., Fischer, B., Spiekers, H., Steingass, H., Meyer, U., Chassot, A., Jilg, T., Omermaier, A., Gruggenberg, T., 2004. Vorhersage der Futteraufnahmevon Milchkühen-Datenbasis von 10 Forschungs-und Universitätsinstituten Deutschlands. VDLUFA Schriftenr 60, 484–504.Jensen, L.M., Nielsen, N.I., Nadeau, E., Markussen, B., Norgaard, P., 2015. Evaluation of five models predicting feed intake by dairy cows fed total mixed rations. LivestockScience 176, 91–103.NRC, 2001. Nutrient requirements of dairy cattle. Seventh Revised Edition, 2001. The National Academies Press.Reed, K.F., Casper, D.P., France, J., Kebreab, E., 2015. Prediction of nitrogen efficiency in dairy cattle: a review. CAB Reviews 10, 1–12.doi: 10.1016/j.anscip.2022.07.44858. Diurnal variation in water-soluble carbohydrate contents of pasture affects milk production of dairy cattle: a simulationapproachG. Menegazzi a,b,*, P.Y. Giles b, D.A. Mattiauda b, F. Elizondo b, T.C.M. Genro c, W.J.J. Gerrits a, J. Dijkstra a, P. Chilibroste ba Animal Nutrition Group, Wageningen University and Research, 6700AH Wageningen, the Netherlandsb Facultad de Agronomía, Universidad de la República, 60000 Paysandú, Uruguayc Empresa Brasileira de Pesquisa Agropecuária, 96401-970 Bagé, Brazil* Corresponding author: Gabriel MenegazziE-mail: gmenegazzi@fagro.edu.uyIntroductionSward height is strongly related to the daily DM intake (DMI) of grazing dairy cows, consequently determining animal performance.Menegazzi et al. (2021) evaluated post-grazing sward heights of Lolium arundinaceum pasture, viz. 15 (TL) and 12 (TM) cm, with unsup-Table 1Summary of DMI (kg/d), DIM (d), MY (kg/d), BW (kg) and parity from 136 and 275 lactations for primiparous and multiparous cows, respectively. Data recorded during thewinters of 2015 to 2021.Item Primiparous MultiparousDMI DIM MY BW DMI DIM MY BW ParityN 10912 10912 10912 10466 18371 18371 18371 17763 18371Mean 18.45 87.03 28.61 611.71 22.53 110.32 33.32 700.34 3.33SD 3.03 62.06 5.38 57.15 3.54 69.14 7.10 62.65 1.48CV, % 16.42 71.31 18.80 9.34 15.71 62.67 21.31 8.95 44.44Table 2Accuracy and precision of models predicting DMI in dairy cows.Equation Mean DMI (kg/d) N RMSPE, kg/d1 RMSPE, %2 ECT, %3 ER, %3 ED, %3Obs. Pred.PrimiparousAgroscope, 1994 18.45 18.44 10912 2.50 13.52 <0.01 17.23 82.77NRC, 2001 18.45 20.61 10466 3.78 20.49 32.57 1.95 65.48Gruber et al., 2004 18.65 20.20 7514 3.09 16.58 25.21 6.30 68.48CSIRO, 2007 18.45 20.46 10466 3.41 18.47 34.97 12.77 52.26FEDNA, 2009 18.45 20.61 10466 3.78 20.49 32.57 1.95 65.48MultiparousAgroscope, 1994 22.53 22.56 18371 3.02 13.42 0.01 19.95 80.05NRC, 2001 22.54 24.56 17763 4.23 18.79 22.77 0.01 77.23Gruber et al., 2004 22.70 25.43 13290 4.62 20.34 35.10 1.86 63.04CSIRO, 2007 22.54 21.04 17763 3.91 17.33 14.69 12.92 72.40FEDNA, 2009 22.54 24.56 17763 4.23 18.79 22.77 0.01 77.231 RMSPE: Root mean square prediction error.2 RMSPE, %: RMSE expressed as a percentage of the observed mean value.3 Error decompositions are expressed as a percentage of mean square prediction error.579 Animal - Science Proceedings 13 (2022) 511–616579plemented dairy cows. Pasture DMI was equal for TM and TL, but milk production (MP) was 2.5 L/d higher on TL. Grazing time of TL cows inthe first evening meal (170 min) was twice that in the morning (92 min), while TM cows had a more equal distribution of grazing timeacross the day (186 and 170 min for morning and evening meal, respectively), and grass digestibility was higher on TL than on TM. Swardchemical composition changes through the day with high contents of DM and water-soluble carbohydrates (WSC) in the evening. Wehypothesised that a higher WSC content in the evening, associated with a higher CP level in TL selected grass, leads to greater level ofrumen fermentation and digestion of TL grass, increasing MP at similar DMI. Simulations were performed using a translated version(Ahmadi et al., 2018) of CTR Dairy model (Chilibroste et al., 2008) to analyse if the observed differences in MP between the TM and TL treat-ment could be explained by the integration of actual cows’ grazing behaviour and diurnal variation in pasture nutrient content.Material and methodsThe CTR Dairy simulates the availability of nutrients to lactating dairy cows fed discontinuously. The model structure considers the input ofup to three different feeds fed independently at any time during the day and predicts release of soluble components from the feeds in therumen, fermentation, and absorption of fermentation end products. The pasture intake pattern was constructed based on the observedgrazing behaviour and daily DMI (17.8±0.64 kg DM/day) of the TM and TL cows (618 kg of BW, 224 DIM). The observed MP was16.2±0.56 (TM) and 18.7±0.56 l/day (TL). Three different contents of WSC in the grass were assumed according to the time of the day wheregrazing was observed, viz. 0000 to 1059 h, 120 g/kg DM; 1100 to 1400 h, 170 g/kg DM; 1700 to 2200 h, 212 g/kg DM; based on Abrahamseet al. (2009) and Cajarville et al. (2015). The CP (120 and 127 g/kg DM for TM and TL, respectively) and NDF (558 and 581 g/kg DM for TMand TL, respectively) were according to Menegazzi et al. (2021) and assumed to be constant during the day.Results and discussionThe predicted MP based on available glucose was 14.9 L/day for TM and 17.0 L/day for TL. The differences between the treatments agreewell with the observed MP. Controversially, Menegazzi et al. (2021) reported that the NDF of the selected diet and the diet digestibility washigher on TL than on TM. The authors argued that probably a better match of nutrients along the day in the rumen of TL cows allowed abetter diet digestibility despite the higher level of NDF. This corroborates the higher absorption of nutrients predicted by the model, whichwas 6 and 9% higher on TL than TM, for absorption of amino acids and glucose plus propionate, respectively. The higher observed CP andsimulatedWSC intake added to shorter grazing meals followed by a ruminating session determined a better ruminal environment and thusa higher extent of fermentation on TL than on TM cows.Conclusion and implicationsThe model allowed integration of actual grazing behaviour and diurnal variation in pasture nutrient content to understand and predict dif-ferences in MP between grazing treatments. The integration between grazing behaviour and sward characteristics can generate opportu-nities to achieve greater efficiency in the use of nutrients throughout a grazing dairy system.Financial supportThe authors thank the ANII for funding the post-graduate scholarship awarded to GM (POS_EXT_2020_1_164958).ReferencesAbrahamse, P.A., Tamminga, S., Dijkstra, J., 2009. Effect of daily movement of dairy cattle to fresh grass in morning or afternoon on intake, grazing behaviour, rumenfermentation and milk production. Journal of Agricultural Science 147, 721–730.Ahmadi, A., Robinson, P.H., Elizondo, F., Chilibroste, P., 2018. Implementation of CTR dairy model using the visual basic for application language of Microsoft excel.International Journal of Agricultural and Environmental Information Systems 9, 74–86.Cajarville, C., Britos, A., Errandonea, N., Gutiérrez, L., Cozzolino, D., Repetto, J.L., 2015. Diurnal changes in water-soluble carbohydrate concentration in lucerne and tall fescuein autumn and the effects on in vitro fermentation. New Zealand Journal of Agricultural Research 58, 281–291.Chilibroste, P., Dijkstra, J., Robinson, P.H., Tamminga, S., 2008. A simulation model ‘CTR Dairy’ to predict the supply of nutrients in dairy cows managed under discontinuousfeeding patterns. Animal Feed Science and Technology 143, 148–173.Menegazzi, G., Giles, P.Y., Oborsky, M., Fast, O., Mattiauda, D.A., Genro, T.C.M., Chilibroste, P., 2021. Effect of post-grazing sward height on ingestive behavior, dry matterintake, and milk production of Holstein dairy cows. Frontiers in Animal Science 2, 1–12.doi: 10.1016/j.anscip.2022.07.44959. Additive bayesian network for systems-oriented meta-analysisB.R. dos Reis *,1, S. Sujani, R.R. WhiteDepartment of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, USA* Corresponding author: Barbara Roqueto dos ReisE-mail: barbarar@vt.eduIntroductionTraditional meta-analysis approaches can be used to explore the associations among diet, rumen, and performance variables; however,model selection in these analyses is somewhat subjective and the data structure often presupposes collinearity among variables which pre-sents challenges for fitting and interpretation. An alternative approach to exploring associations among diet, rumen, and performance is toleverage network analysis, specifically additive Bayesian networks allow for the inclusion of intercepts for grouping variables (i.e., study1 Present address: Food Science & Technology, 360 Duck Pond Dr, Blacksburg, VA 24060, USA.580 Animal - Science Proceedings 13 (2022) 511–616580

Diurnal variation in water-soluble carbohydrate contents of pasture affects milk production of dairy cattle: a simulation approach.

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Diurnal variation in water-soluble carbohydrate contents of pasture affects milk production of dairy cattle: a simulation approach.

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