Nghiên cứu khả năng sinh trưởng, sinh sản và năng suất chất lượng sữa của bò cái holstein friesian thuần, các thế hệ lai f1, f2, f3 giữa HF và lai sind nuôi tại tỉnh lâm đồng

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B GIÁO D C VÀ ðÀO T O TRƯ NG ð I H C NÔNG NGHI P HÀ N I TR N QUANG H NH NGHIÊN C U KH NĂNG SINH TRƯ NG, SINH S N, NĂNG SU T VÀ CH T LƯ NG S A C A BÒ CÁI HOLSTEIN FRIESIAN (HF) THU N, CÁC TH H LAI F1, F2 VÀ F3 GI A HF VÀ LAI SIND NUÔI T I T NH LÂM ð NG LU N ÁN TI N SĨ NÔNG NGHI P Chuyên ngành: CHĂN NUÔI ð NG V T Mã s : 62.40.01 Ngư i hư ng d n khoa h c: GS.TS ð NG VŨ BÌNH HÀ N I – 2010 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… i L I CAM ðOAN - Tôi xin cam đoan cơng trình nghiên c u c a riêng Các s li u k t qu nêu lu n án trung th c chưa t ng đư c cơng b b t kỳ cơng trình khác - M i s giúp ñ cho vi c th c hi n lu n án ñã ñư c c m ơn tài li u tham kh o đư c trích d n lu n án ñ u ñư c ch rõ ngu n g c xu t x th c t rõ ràng Tác gi lu n án Tr n Quang H nh Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… ii L I C M ƠN Trư c tiên xin chân thành c m ơn GS.TS ð ng Vũ Bình - ngư i hư ng d n khoa h c - t n tình hư ng d n đóng góp nhi u ý ki n h t s c quý báu Cho phép bày t l i c m ơn t i Ban giám hi u, Vi n ðào t o Sau ð i h c, Khoa Chăn nuôi & Nuôi tr ng Th y s n, th y cơ, b n đ ng nghi p B môn Di truy n & Ch n gi ng V t nuôi, d án PHE, Trư ng ð i h c Nông nghi p Hà N i; Ban giám hi u, Ban ch nhi m Khoa Chăn nuôi - Thú y, Trư ng ð i h c Tây Nguyên, ñã cho phép t o m i ñi u ki n thu n l i giúp đ tơi q trình nghiên c u hồn thành lu n án Tôi xin c m ơn Ban giám đ c, phịng K thu t c a Chi c c Thú Y, Công ty Thanh Sơn (Vi t Nam – Hà Lan), Công ty C ph n S a t nh Lâm ð ng h nuôi bò s a thành ph ðà L t, huy n ð c Tr ng, ðơn Dương, Lâm Hà, B o L c… ñã t o m i ñi u ki n thu n l i cho ti n hành thí nghi m, thu th p s li u làm s cho b n lu n án C m ơn Gia đình b n đ ng nghi p đ ng viên khích l , t o m i ñi u ki n thu n l i góp ph n cho b n lu n án ñư c hoàn thành Hà N i, ngày tháng năm 2010 Tác gi lu n án Tr n Quang H nh Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… iii M CL C Trang L i cam ñoan i L i c m ơn ii M cl c iii Danh m c ch vi t t t vi Danh m c b ng vii Danh m c bi u ñ ix Danh m c hình x ð TV Nð Chương T NG QUAN 1.1 CƠ S LÝ THUY T C A V N ð NGHIÊN C U 1.1.1 Tính tr ng s lư ng s di truy n c a tính tr ng s lư ng 1.1.2 Lai t o gi ng 1.2 SINH TRƯ NG, SINH S N, NĂNG SU T VÀ CH T LƯ NG S A C A BÒ S A 1.2.1 Sinh trư ng 7 1.2.2 Sinh s n 13 1.2.3 Năng su t ch t lư ng s a 18 1.3 TÌNH HÌNH NGHIÊN C U TRONG VÀ NGỒI NƯ C 32 1.3.1 Tình hình nghiên c u ngồi nư c 32 1.3.2 Tình hình nghiên c u nư c 34 1.4 M T S Y U T V ðI U KI N T NHIÊN T NH LÂM ð NG 38 1.4.1 ð a hình 38 1.4.2 Khí h u 38 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… iv 1.4.3 M t s nét v tình hình chăn ni bị s a s d ng th c ăn c a t nh Lâm ð ng Chương V T LI U VÀ PHƯƠNG PHÁP NGHIÊN C U 2.1 V T LI U NGHIÊN C U 40 42 42 2.1.1 Bò HF (Holstein Friesian) 42 2.1.2 Nhóm bị lai hư ng s a 43 2.2 N I DUNG NGHIÊN C U 45 2.3 PHƯƠNG PHÁP NGHIÊN C U 46 2.3.1 Kh sinh trư ng 47 2.3.2 Kh sinh s n 48 2.3.3 Kh s n xu t s a 49 2.3.4 Tiêu t n th c ăn 50 2.4 X LÝ S LI U Chương K T QU VÀ TH O LU N 3.1 51 53 KH NĂNG SINH TRƯ NG C A BÊ, BÒ CÁI F1, F2, F3 (HF x LAI SIND) VÀ HF 53 3.1.1 Kh sinh trư ng c a nhóm bê, bị theo dõi 53 3.1.2 Kh sinh trư ng c a nhóm bê, bị thí nghi m 60 3.2 KH NĂNG SINH S N C A BÒ CÁI F1, F2, F3 (HF x LAI SIND) VÀ HF 77 3.2.1 Tu i ph i gi ng l n ñ u 77 3.2.2 Tu i ñ l a ñ u 78 3.2.3 Th i gian ph i l i sau ñ 81 3.2.4 Kho ng cách gi a l a ñ 83 3.2.5 H s ph i gi ng 86 3.3 KH NĂNG S N XU T S A C A BÒ CÁI F1, F2, F3 (HF x LAI SIND) VÀ HF 88 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… v 3.3.1 S n lư ng s a th c t th i gian cho s a 88 3.3.2 S n lư ng s a 305 ngày 92 3.3.3 S n lư ng s a tiêu chu n (4% m ) 96 3.3.4 S n lư ng s a qua l a ñ 97 3.3.5 Năng su t s a qua tháng c a chu kỳ 305 ngày 100 3.3.6 Ch t lư ng s a 109 3.3.7 Tiêu t n th c ăn cho cho 1kg s a 118 K T LU N VÀ ð NGH 124 K T LU N 124 ð NGH 126 Các công trình cơng b có liên quan đ n lu n án 127 Tài li u tham kh o 128 Ph l c 154 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… vi DANH M C CÁC CH VI T T T CK : Ch t khô CSDT CSKL CSTM CV Cv% DTC ðVT EXP : Ch s dài thân : Ch s kh i lư ng : Ch s trịn : Cao vây : H s bi n sai : Dài thân chéo : ðơn v tính : Exponent – s mũ F1 F2 : Con lai gi a bò HF bò lai Sind : Con lai gi a bò HF bò F1 F3 HSSS HF : Con lai gi a bò HF bò F2 :H s s ts a : Holstein Friesian KHKT KL : Khoa h c k thu t : Kh i lư ng Max Min NLTð NXB PTNT SE TB TT : Maximum – C c ñ i : Minimum – C c ti u : Năng lư ng trao ñ i : Nhà xu t b n : Phát tri n nông thôn : Standard Error – Sai s tiêu chu n : Trung bình : Tăng trư ng TTTA VCK : T ng tiêu t n th c ăn : V t ch t khô VCKKM VN : V t ch t khơ khơng m : Vịng ng c : Trung bình Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… vii DANH M C CÁC B NG STT Tên b ng Trang 2.1 S m u nghiên c u c a ñ tài 45 3.1 Kh i lư ng bò (kg) t sơ sinh ñ n 24 tháng tu i 53 3.2 Tăng trư ng t ñ i (g/ngày) tăng trư ng tương đ i (%) c a nhóm bị 3.3 55 Kích thư c (cm) m t s chi u ño qua tháng tu i c a nhóm bò 58 3.4 M t s ch s c u t o th hình c a nhóm bị 59 3.5 Kh i lư ng bò (kg) t sơ sinh ñ n 24 tháng tu i 60 3.6 Tăng trư ng truy t ñ i (g/ngày) tăng trư ng tương đ i (%) c a nhóm bị 3.7 Kích thư c m t s chi u đo (cm) c a nhóm bị qua tháng tu i 3.8 63 66 M t s ch s c u t o th hình c a nhóm bò qua tháng tu i 67 3.9 Hàm sinh trư ng c a bò lai HF 70 3.10 Tu i, kh i lư ng tăng kh i lư ng c c ñ i t i ñi m u n 76 3.11 Tu i ph i gi ng l n ñ u 77 3.12 Tu i ñ l a ñ u 79 3.13 Th i gian ph i l i (ngày) sau ñ 82 3.14 Kho ng cách gi a l a ñ 83 3.15 H s ph i gi ng c a nhóm bị 86 3.16 S n lư ng s a th c t th i gian cho s a 89 3.17 S n lư ng s a th c t th i gian cho s a 90 3.18 S n lư ng s a (kg/chu kỳ 305 ngày) c a nhóm bị 92 3.19 S n lư ng s a tiêu chu n 305 ngày (4% m ) 96 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… viii 3.20 S n lư ng s a qua l a ñ 3.21 Năng su t s a (kg) h s s t s a (HSSS) qua tháng c a chu kỳ 305 ngày 3.22 98 101 Năng su t s a (kg) h s s t s a (HSSS) theo tháng c a chu kỳ 305 ngày 102 3.23 T l (%) su t s a bò qua tháng so v i c chu kỳ 107 3.24 T tr ng c a s a (s li u theo dõi) 109 3.25 T l v t ch t khô không m c a s a (s li u theo dõi) 110 3.26 T l m s a (s li u theo dõi) 112 3.27 T l protein s a (s li u theo dõi) 114 3.28 Ch t lư ng s a l a th nh t c a bị ni thí nghi m 117 3.29 Tiêu t n th c ăn tinh cho kg s a 118 3.30 Tiêu t n th c ăn s cho 1kg s a 119 3.31 Tiêu t n th c ăn cho 1kg s a (th c ăn tinh th c ăn s ) 120 3.32 Ư c tính chi phí th c ăn (v t ch t khô) cho 1kg s a 121 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p………… ix DANH M C CÁC BI U ð STT Tên bi u ñ Trang 3.1 Tăng trư ng t đ i c a nhóm bị 56 3.2 Tăng trư ng t đ i c a nhóm bị 65 3.3 T l su t s a theo tháng cho s a 108 3.4 T l su t s a theo tháng cho s a 108 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 157 B ng Năng su t s a theo tu n (s li u ni thí nghi m) F1 F2 F3 HF F1 F2 F3 HF F1 F2 F3 HF 123,80 129,30 143,40 144,30 11 132,50 133,00 141,10 149,40 21 97,60 106,20 110,20 117,60 31 80,40 83,20 93,80 96,50 120,30 129,60 144,00 152,00 12 133,30 138,50 154,00 156,90 22 98,80 101,90 116,60 115,60 32 81,60 89,00 93,90 101,80 124,20 133,60 148,10 145,20 13 128,10 129,10 139,20 141,10 23 101,50 105,90 107,60 115,60 33 76,50 73,10 82,00 84,60 Tu n/Năng su t s 132,50 133,30 133,90 138,90 151,90 144,80 149,90 150,30 151,40 155,00 152,80 155,10 14 15 16 122,40 120,80 129,60 125,30 126,70 124,60 139,70 132,30 133,30 143,30 146,60 139,00 24 25 26 96,20 96,50 97,20 102,60 95,50 97,40 114,00 110,30 111,30 120,20 107,20 116,60 34 35 36 74,50 71,70 69,10 77,00 79,70 74,90 79,10 78,31 76,60 84,50 78,90 82,10 a 135,10 142,40 154,30 157,30 14 114,40 117,00 125,30 126,80 27 90,30 94,20 104,80 110,30 37 56,50 62,60 69,10 70,00 144,00 137,70 156,30 167,30 18 114,90 115,90 124,90 129,10 28 87,80 99,60 106,10 113,70 38 58,20 60,40 66,20 70,20 134,40 138,90 141,90 149,00 19 107,90 116,00 122,90 126,50 29 84,40 86,50 93,90 97,10 39 55,40 58,40 63,70 69,70 180 160 140 F1 N ă n g s u t s a (k g ) Nhóm bị F1 F2 F3 HF 120 F2 100 F3 HF 80 60 40 10 13 16 19 22 25 28 31 34 37 40 Tu n Hình Năng su t s a theo tu n (s li u ni thí nghi m) 10 126,70 136,80 154,00 157,00 20 112,20 114,90 124,70 128,80 30 84,30 89,30 93,80 100,10 40 55,50 59,10 61,90 66,40 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 158 B ng H s tương quan gi a su NSSTT c a nhóm bò VCKKM F1 - 0,70 F2 - 0,35 F3 - 0,77 HF - 0,33 K T QU t s a th c t M - 0,60 - 0,62 - 0,46 - 0,91 v i ch t lư ng s a Protein T tr ng - 0,70 - 0,09 - 0,50 - 0,33 - 0,29 - 0,84 - 0,70 - 0,84 CH Y HÀM GOMPERTZ TRÊN STATGRAPHICS CENTURION XV v 15.1.02 4.1 K t qu ch y hàm Gompert c a nhóm bị theo dõi Nonlinear Regression - KLF1TD Dependent variable: KLF1TD Independent variables: TTF1TD Function to be estimated: M*EXP(-A*EXP(-B*TTF1TD)) Estimation method: Marquardt Estimation stopped due to convergence of residual sum of squares Number of iterations: Number of function calls: 28 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 420.804 4.89388 411.722 430.067 A 2.37423 0.0233495 2.32837 2.4201 B 0.104943 0.00209512 0.100828 0.109058 Analysis of Variance Source Sum of Squares Df Mean Square Model 3.06549E7 1.02183E7 Residual 113987 567 201.035 Total 3.07689E7 570 Total (Corr.) 7.47363E6 569 R-Squared = 98.3042 percent R-Squared (adjusted for d.f.) = 98.2344 percent Standard Error of Est = 14.1787 Mean absolute error = 11.6272 Durbin-Watson statistic = 0.836953 Lag residual autocorrelation = 0.580415 Residual Analysis Estimation Validation n 570 MSE 201.035 MAE 11.6272 MAPE 11.2176 ME -0.650562 MPE -5.75787 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF1DT and independent variables The equation of the fitted model is KLF1TD = 420.804*EXP(-2.37423*EXP(-0.104943*TTF1TD)) In performing the fit, the estimation process terminated successully after iterations, at which point the estimated coefficients appeared to converge to the current estimates The R-Squared statistic indicates that the model as fitted explains 98.3042% of the variability in KLF1TD The adjusted Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 159 R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.2344% The standard error of the estimate shows the standard deviation of the residuals to be 14.1787 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 11.6272 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters These intervals are approximate and most accurate for large sample sizes You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially K h o i lu o n g b o F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi 18 Nonlinear Regression - KLF2TD Dependent variable: KLF2TD Independent variables: TTF2TD Function to be estimated: M*EXP(-A*EXP(-B*TTF2TD)) Estimation method: Marquardt Estimation stopped due to convergence of residual sum of squares Number of iterations: Number of function calls: 28 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 441.949 5.15766 431.999 452.258 A 2.35978 0.0240481 2.31255 2.40701 B 0.104381 0.00218541 0.100088 0.108673 Analysis of Variance Source Sum of Squares Df Mean Square Model 3.49492E7 1.16497E7 Residual 146839 587 250.151 Total 3.5096E7 590 Total (Corr.) 8.50234E6 589 R-Squared = 98.463 percent R-Squared (adjusted for d.f.) = 98.271 percent Standard Error of Est = 15.8162 24 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 160 Mean absolute error = 12.6605 Durbin-Watson statistic = 0.586476 Lag residual autocorrelation = 0.705686 Residual Analysis Estimation Validation n 590 MSE 250.151 MAE 12.6605 MAPE 11.8795 ME -0.730073 MPE -6.17391 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF2TD and independent variables The equation of the fitted model is KLF2TD = 441.949*EXP(-2.35978*EXP(-0.104381*TTF2TD)) In performing the fit, the estimation process terminated successully after iterations, at which point the estimated coefficients appeared to converge to the current estimates The R-Squared statistic indicates that the model as fitted explains 98.463% of the variability in KLF2TD The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.271% The standard error of the estimate shows the standard deviation of the residuals to be 15.8162 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 12.6605 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters These intervals are approximate and most accurate for large sample sizes You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially K h o i lu o n g b o F (k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi Nonlinear Regression - KLF3TD Dependent variable: KLF3TD Independent variables: TTF3TD Function to be estimated: M*EXP(-A*EXP(-B*TTF3TD)) Estimation method: Marquardt 18 24 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 161 Estimation stopped due to convergence of parameter estimates Number of iterations: Number of function calls: 31 Estimation Results Asymptotic Standard Error 4.61922 0.020509 0.00184746 Parameter Estimate M 478.554 A 2.36299 B 0.105528 Analysis of Variance Sum of Squares Source Model 4.07927E7 Residual 119364 Total 4.09121E7 Total (Corr.) 9.86366E6 Df 577 580 579 Asymptotic Confidence Lower 469.703 2.32271 0.101899 95.0% Interval Upper 487.848 2.40328 0.109156 Mean Square 1.35976E7 206.87 R-Squared = 98.7329 percent R-Squared (adjusted for d.f.) = 98.7237 percent Standard Error of Est = 14.383 Mean absolute error = 11.8348 Durbin-Watson statistic = 0.90785 Lag residual autocorrelation = 0.54493 Residual Analysis Estimation Validation n 580 MSE 206.87 MAE 11.8348 MAPE 10.7227 ME -0.748078 MPE -5.66541 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF3TD and independent variables The equation of the fitted model is KLF3TD = 478.554*EXP(-2.36299*EXP(-0.105528*TTF3TD)) In performing the fit, the estimation process terminated successully after iterations, at which point the residual sum of squares appeared to approach a minimum The R-Squared statistic indicates that the model as fitted explains 98.7329% of the variability in KLF3TD The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.7237% The standard error of the estimate shows the standard deviation of the residuals to be 14.383 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 11.8348 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters These intervals are approximate and most accurate for large sample sizes You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 162 K h o i lu o n g F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi Nonlinear Regression - KLHFTD Dependent variable: KLHFTD Independent variables: TTHFTD Function to be estimated: M*EXP(-A*EXP(-B*TTHFTD)) Estimation method: Marquardt Estimation stopped due to convergence of parameter estimates Number of iterations: Number of function calls: 31 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 498.823 3.38154 492.612 505.867 A 2.36657 0.0151509 2.33188 2.39127 B 0.107524 0.00135165 0.104875 0.110173 Analysis of Variance Source Sum of Squares Df Mean Square Model 1.00592E8 3.35307E7 Residual 344281 1282 268.55 Total 1.00936E8 1285 Total (Corr.) 2.43181E7 1284 R-Squared = 98.5843 percent R-Squared (adjusted for d.f.) = 98.5821 percent Standard Error of Est = 16.3875 Mean absolute error = 13.4649 Durbin-Watson statistic = 0.883896 Lag residual autocorrelation = 0.557709 18 24 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 163 Residual Analysis Estimation Validation n 1285 MSE 268.55 MAE 13.4649 MAPE 11.0368 ME -0.788923 MPE -5.67459 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLHFTD and independent variables The equation of the fitted model is KLHFTD = 498.823*EXP(-2.36657*EXP(-0.107524*TTHFTD)) In performing the fit, the estimation process terminated successully after iterations, at which point the residual sum of squares appeared to approach a minimum The R-Squared statistic indicates that the model as fitted explains 98.5843% of the variability in KLHFTD The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 98.5821% The standard error of the estimate shows the standard deviation of the residuals to be 16.3875 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 13.4649 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters These intervals are approximate and most accurate for large sample sizes You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially Plot of Fitted Model K h o i lu o n g b o H F ( k g ) 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi 18 24 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 164 4.2 K t qu ch y hàm Gompert c a nhóm bị ni thí nghi m Nonlinear Regression - KLF1NTN Dependent variable: KLF1NTN Independent variables: TTNTN Function to be estimated: M*EXP(-A*EXP(-B*TTNTN)) Estimation method: Marquardt Estimation stopped due to convergence of parameter estimates Number of iterations: Number of function calls: 31 Estimation Results Asymptotic Standard Error 7.62082 0.0347721 0.00327434 Asymptotic Confidence Lower 429.544 2.23385 0.0981884 Parameter Estimate M 444.484 A 2.30287 B 0.104687 Analysis of Variance Source Sum of Squares Df Mean Square Model 6.13112E6 2.04371E6 Residual 9480.44 97 97.7365 Total 6.1406E6 100 Total (Corr.) 1.43782E6 99 R-Squared = 99.3406 percent R-Squared (adjusted for d.f.) = 99.327 percent Standard Error of Est = 9.88618 Mean absolute error = 8.64538 Durbin-Watson statistic = 0.352689 Lag residual autocorrelation = 0.817525 Residual Analysis Estimation Validation n 100 MSE 97.7365 MAE 8.64538 MAPE 9.70995 ME -0.700724 MPE -5.2761 95.0% Interval Upper 459.834 2.37188 0.111186 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF1NTN and independent variables The equation of the fitted model is KLF1NTN = 444.484*EXP(-2.30287*EXP(-0.104687*TTNTN)) In performing the fit, the estimation process terminated successully after iterations, at which point the residual sum of squares appeared to approach a minimum The R-Squared statistic indicates that the model as fitted explains 99.3406% of the variability in KLF1NTN The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 99.327% The standard error of the estimate shows the standard deviation of the residuals to be 9.88618 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 8.64538 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters These intervals are approximate and most accurate for large sample sizes You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 165 K h o i lu o n g b o F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi Nonlinear Regression - KLF2NTN Dependent variable: KLF2NTN Independent variables: TTNTD Function to be estimated: M*EXP(-A*EXP(-B*TTNTN)) Estimation method: Marquardt Estimation stopped due to convergence of parameter estimates Number of iterations: Number of function calls: 31 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 468.184 8.03365 452.489 484.422 A 2.37464 0.0410211 2.29322 2.45605 B 0.107303 0.00358498 0.102788 0.117018 Analysis of Variance Source Sum of Squares Df Mean Square Model 7.04729E6 2.3491E6 Residual 12596.6 97 129.862 Total 7.05989E6 100 Total (Corr.) 1.69594E6 99 R-Squared = 99.2372 percent R-Squared (adjusted for d.f.) = 99.2219 percent Standard Error of Est = 11.3957 Mean absolute error = 9.7617 18 24 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 166 Durbin-Watson statistic = 0.711779 Lag residual autocorrelation = 0.613465 Residual Analysis Estimation Validation n 100 MSE 129.862 MAE 9.7617 MAPE 9.26129 ME -0.717794 MPE -5.01688 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF2NTN and independent variables The equation of the fitted model is KLF2NTN = 468.184*EXP(-2.37464*EXP(-0.107303*TTNTN)) In performing the fit, the estimation process terminated successully after iterations, at which point the residual sum of squares appeared to approach a minimum The R-Squared statistic indicates that the model as fitted explains 99.2372% of the variability in KLF2NTN The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 99.2219% The standard error of the estimate shows the standard deviation of the residuals to be 11.3957 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 9.7617 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters These intervals are approximate and most accurate for large sample sizes You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially K h o i lu o n g b o F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi 18 24 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 167 Nonlinear Regression - KLF3NTN Dependent variable: KLF3NTN Independent variables: TTNTN Function to be estimated: M*EXP(-A*EXP(-B*TTNTN)) Estimation method: Marquardt Estimation stopped due to convergence of parameter estimates Number of iterations: Number of function calls: 31 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 490.214 8.3501 473.801 506.946 A 2.37103 0.040621 2.29041 2.45165 B 0.107915 0.00355836 0.102852 0.116977 Analysis of Variance Sum of Squares Df Mean Square Source Model 7.73198E6 2.57733E6 Residual 13610.8 97 140.317 Total 7.74559E6 100 Total (Corr.) 1.86283E6 99 R-Squared = 99.3094 percent R-Squared (adjusted for d.f.) = 99.2143 percent Standard Error of Est = 11.8455 Mean absolute error = 10.3627 Durbin-Watson statistic = 0.612331 Lag residual autocorrelation = 0.684735 Residual Analysis Estimation Validation n 100 MSE 140.317 MAE 10.3627 MAPE 10.4534 ME -0.855128 MPE -5.99548 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLF3NTN and independent variables The equation of the fitted model is KLF3NTN = 490.214*EXP(-2.37103*EXP(-0.107915*TTNTN)) In performing the fit, the estimation process terminated successully after iterations, at which point the residual sum of squares appeared to approach a minimum The R-Squared statistic indicates that the model as fitted explains 99.3094% of the variability in KLF3NTN The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 99.2143% The standard error of the estimate shows the standard deviation of the residuals to be 11.8455 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 10.3627 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters These intervals are approximate and most accurate for large sample sizes You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 168 K h o i lu o n g b o F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi Nonlinear Regression - KLHFNTN Dependent variable: KLHFNTN Independent variables: TTNTN Function to be estimated: M*EXP(-A*EXP(-B*TTNTN)) Estimation method: Marquardt Estimation stopped due to convergence of residual sum of squares Number of iterations: Number of function calls: 32 Estimation Results Asymptotic 95.0% Asymptotic Confidence Interval Parameter Estimate Standard Error Lower Upper M 522.868 8.78139 505.71 540.607 A 2.41096 0.0410924 2.32941 2.49252 B 0.109181 0.00350163 0.103231 0.117131 Analysis of Variance Source Sum of Squares Df Mean Square Model 8.71709E6 2.9057E6 Residual 14864.8 97 153.246 Total 8.73195E6 100 Total (Corr.) 2.1363E6 99 R-Squared = 99.3542 percent R-Squared (adjusted for d.f.) = 99.2698 percent Standard Error of Est = 12.3792 Mean absolute error = 10.8623 18 24 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nông nghi p…………… 169 Durbin-Watson statistic = 0.512386 Lag residual autocorrelation = 0.733998 Residual Analysis Estimation Validation n 100 MSE 153.246 MAE 10.8623 MAPE 11.0878 ME -0.975136 MPE -6.6848 The StatAdvisor The output shows the results of fitting a nonlinear regression model to describe the relationship between KLHFNTD and independent variables The equation of the fitted model is KLHFNTN = 522.868*EXP(-2.41096*EXP(-0.10981*TTNTN)) In performing the fit, the estimation process terminated successully after iterations, at which point the estimated coefficients appeared to converge to the current estimates The R-Squared statistic indicates that the model as fitted explains 99.3542% of the variability in KLHFNTN The adjusted R-Squared statistic, which is more suitable for comparing models with different numbers of independent variables, is 99.2698% The standard error of the estimate shows the standard deviation of the residuals to be 12.3792 This value can be used to construct prediction limits for new observations by selecting the Forecasts option from the text menu The mean absolute error (MAE) of 10.8623 is the average value of the residuals The Durbin-Watson (DW) statistic tests the residuals to determine if there is any significant correlation based on the order in which they occur in your data file The output also shows aymptotic 95.0% confidence intervals for each of the unknown parameters These intervals are approximate and most accurate for large sample sizes You can determine whether or not an estimate is statistically significant by examining each interval to see whether it contains the value Intervals covering correspond to coefficients which may well be removed form the model without hurting the fit substantially K h o i lu o n g b o H F ( k g ) Plot of Fitted Model 500 450 400 350 300 250 200 150 100 50 0 12 Thang tuoi 18 24 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nơng nghi p…………… 170 M T S HÌNH NH Hình Bị F1 Hình Bị F2 Trư ng ð i h c Nông nghi p Hà N i – Lu n án ti n s khoa h c nơng nghi p…………… 171 Hình Bị F3 Hình Bị HF ... BÊ, BÒ CÁI F1, F2, F3 (HF x LAI SIND) VÀ HF 53 3.1.1 Kh sinh trư ng c a nhóm bê, bị theo dõi 53 3.1.2 Kh sinh trư ng c a nhóm bê, bị thí nghi m 60 3.2 KH NĂNG SINH S N C A BÒ CÁI F1, F2, F3 (HF. .. ðơn v tính : Exponent – s mũ F1 F2 : Con lai gi a bò HF bò lai Sind : Con lai gi a bò HF bò F1 F3 HSSS HF : Con lai gi a bò HF bò F2 :H s s ts a : Holstein Friesian KHKT KL : Khoa h c k thu t :... p………… ‘? ?Nghiên c u kh sinh trư ng, sinh s n, su t ch t lư ng s a c a bò Holstein Friesian( HF) thu n, th h lai F1, F2 F3 gi a HF lai Sind nuôi t i t nh Lâm ð ng” M c ñích nghiên c u ðánh giá kh sinh