Partitioning genetic variance of metabolizable energy efficiency in dairy cows
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1 Partitioning genetic variance of metabolizable energy efficiency in dairy cows Mehtiö, T., Negussie, E., Mäntysaari, P., Mäntysaari, E.A., Lidauer, M.H.* Natural Resources Institute Finland (Luke) EAAP 67th Annual Meeting, Belfast /12/2016
2 Motivation A cow has different pathways of energy use Could we estimate partial efficiencies for different energy pathways? it would increase understanding of cows feed efficiency supports defining of the breeding goal breeding values for partial efficiencies 2
3 Finnish feeding is based on metabolizable energy Feed gross energy intake Faecal energy Digestible energy 100% Metabolizable energy is calculated for each diet Metabolizable energy 3 Urine energy CH 4 energy MJ per kg BW MJ per kg gain 5.15 MJ per kg ECM Growth Maintenance Milk Feeding requirement norms are based on metabolizable energy
4 Energy pathways of a lactating cow Feed gross energy intake 100% Faecal energy 95 MJ Digestible energy 26% 15 MJ 22 MJ Metabolizable energy 4 Urine energy CH 4 energy Body weight: 592 kg Growth: 0.5 kg / day Milk yield: 30 kg ECM / day 4% 6% MJ per kg BW MJ per kg gain 5.15 MJ per kg ECM 62 MJ 17 MJ 366 MJ 271 MJ 234 MJ 155 MJ 17% Maintenance Growth Milk 5% 42%
5 AIM Assessing partial efficiencies for different energy pathways in dairy cows by random regression analyses 5
6 Metabolizable energy efficiency Feed gross energy intake 100% Digestible energy Metabolizable energy Modelling metabolizable energy (ME) intake with regressions on energy pathways in the model Including random regressions should allow estimation of partial efficiencies Expectation for ME intake e 1 BW a 1i BW 0.75 Maintenance e 2 BWgain +a 2i BWgain Growth e 3 ECM +a 3i ECM Milk 6
7 Used feed efficiency data From Luke s research farms Rehtijärvi & Minkiö 495 primiparous Nordic Red dairy cows weekly observations (averages of daily observations) Recorded from lactation week 2 to 40 Means for different variables by lactation weeks Lactation weeks 7 Metabolizable energy intake (MJ) Energy corrected milk (kg) 12/10/2016 Metabolic body weight (kg) Body weight gain (kg) Body weight loss (kg) Residual energy intake (MJ) ALL
8 Applied Statistical Models 1) Metabolizable Energy Efficiency (MEE) Repeatability animal model for metabolizable energy intake Expectation for intake modelled by regressions on energy pathways MMMMMM iiiiiiii = rrrrrr ii + llll jj + ee 11ss BBBB iiiiii llllllllllll ss + ee 22ss EEEEEE iiiiii llllllllllll ss + ee 33ss BBBBBB iiiiii llllllllllll ss + ee 44ss BBBBBB iiiiii llllllllllll ss + aa ll + pppp ll + εε iiiiiiii 8
9 Applied Statistical Models 2) Partial Metabolizable Energy Efficiency (pmee) Random regression animal model for metabolizable energy intake Expectation for intake modelled by regressions on energy pathways Random regressions for animal effects MMMMMM iiiiiiii = rrrrrr ii + llll jj + ee 11ss BBBB iiiiii llllllllllll ss + ee 22ss EEEEEE iiiiii llllllllllll ss + ee 33ss BBBBBB iiiiii llllllllllll ss + ee 44ss BBBBBB iiiiii llllllllllll ss + aa 00ll + aa 11ll BBBB iiiiii + aa 22ll EEEEEE iiiiii + aa 33ll BBBBBB iiiiii + aa 44ll BBBBBB iiiiii + pppp 00ll + pppp 11ll BBBB iiiiii + pppp 22ll EEEEEE iiiiii + pppp 33ll BBBBBB iiiiii + pppp 44ll BBBBBB iiiiii + εε iiiiiiii 9
10 Applied Statistical Models 3) Residual Energy Intake (REI) Repeatability animal model for residual energy intake Served as reference analysis RRRRRR iiiiii = rrrrrr ii + llll jj + aa ll + pppp ll + εε iiiiii 10
11 Analyses Restricted Maximum Likelihood analyses (AI-REML, EM-REML) [DMU package, Madsen & Jensen, 2013] Different random regression models were fitted Akaike s information criterion (AIC) for model comparison Heritabilities based on random regression models were calculated for a standard lactating cow Metabolic body weight = kg Energy corrected milk yield = 28.5 kg / day Body weight gain = 0.27 kg / day 11
12 Results Variances and heritabilities Model Additive Genetic Animal Effects σ 2 a σ 2 pe σ 2 e h 2 η 2 Int. η 2 MBW η 2 ECM η 2 BWG η 2 BWL AIC REI Intercept MEE Intercept pmee1 Intercept, MBW pmee2 Intercept, ECM pmee3 Intercept, MBW, ECM pmee4 Intercept, MBW, ECM, BWG, BWL pmee5 MBW, ECM
13 Results Correlations Genetic (upper tr.) and permanent environmental (lower tr.) correlations between partial efficiencies and overall efficiency pmee(mbw) pmee(ecm) MEE(overall) pmee(mbw) pmee(ecm) MEE(overall) Genetic standard deviations of partial regression coefficients σ aa pmee(mbw) σ aa pmee(ecm) σ aa (overall) 0.10 MJ / kg MBW / day 0.39 MJ / kg ECM / day 11.5 MJ /day 13
14 Conclusions Models for metabolizable energy efficiency resulted better fit Partial energy efficiencies can be modelled by random regressions Additive genetic variance was almost entirely explained by regressions on maintenance and milk yield Additional analyses are needed for verifying the genetic correlations between partial efficiencies 14 12/10/2016
15 Thank you for your attention Our gratitude to the co-funders of this study 15
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