Growth performance of different chicken strains in ACGG

Growth performance of different chicken strains in ACGG project countries By Group: 5

Summary statistics • Bird_Unique_ID bweight_tag Birth. date onsta_country. x Length: 28834 Min. : 1. 000 • Class : character 1 st Qu. : 1. 000 • Mode : character Median : 2. 000 • Mean : 1. 923 • 3 rd Qu. : 3. 000 • Max. : 3. 000 • •

Summary statistics…. • • onsta_station. x onsta_pen bweight_measurement_date 1: 7221 198 : 1001 Length: 28834 2: 5476 197 : 918 Class : character 4: 5671 181 : 798 Mode : character 5: 1930 5 : 732 6: 8536 11 : 732 4 : 695 (Other): 23958

Summary statistics…. • • bweight_measurement_age bweight_weight bweight_remark Min. : 1. 00 Min. : 0 Length: 28834 Mode: logical 1 st Qu. : 7. 00 1 st Qu. : 400 Class : character NA's: 28834 Median : 15. 00 Median : 1245 Mode : character Mean : 16. 39 Mean : 1420 3 rd Qu. : 23. 00 3 rd Qu. : 2351 Max. : 46. 00 Max. : 4993 X

Summary statistics…. • tag_number onsta_country. y onsta_station. y breg_strain • Length: 28834 • Class : character 1 st Qu. : 1. 000 2 : 2744 • Mode : character Median : 2. 000 Median : 4. 000 4 : 8952 • Mean : 1. 923 Mean : 3. 528 7 : 1317 • 3 rd Qu. : 3. 000 3 rd Qu. : 6. 000 8 : 3923 • Max. : 3. 000 Max. : 6. 000 9 : 2714 Min. : 1. 000 1 : 8758 10: 426 • • birth_date hatch_weight • Length: 28834 • Class : character 1 st Qu. : 32. 00 • Mode : character Median : 38. 00 Min. : 16. 00 • Mean : 37. 56 • 3 rd Qu. : 42. 00 • Max. : 91. 00 •

Overall linear regression model body weight • lm(formula = bweight_weight ~ bweight_measurement_age + onsta_station. x + onsta_pen + breg_strain, data = dbs) • Model: yijkl=µ +βij+A(i)+S(j)+P(s) +D(k), • Where: • Yijkl= • µ= • A(i)= • S(j)= • P(s)= • D(k)=

Residuals: • Min 1 Q Median 3 Q Max • -2680. 66 -384. 64 -96. 16 349. 80 2528. 84 • • Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1434. 0815 19. 2269 74. 587 <2 e-16 *** bweight_measurement_age 70. 6264 0. 3344 211. 199 <2 e-16 *** onsta_station. x -530. 4435 25. 6629 -20. 670 <2 e-16 *** onsta_pen 6. 9048 0. 6887 10. 027 <2 e-16 *** breg_strain 0. 5558 1. 7128 0. 324 0. 746 • Residual standard error: 605. 8 on 28829 degrees of freedom • Multiple R-squared: 0. 7029, Adjusted R-squared: 0. 7029 • F-statistic: 1. 705 e+04 on 4 and 28829 DF, p-value: < 2. 2 e-16

Linear regression between station • lm(formula = bweight_weight ~ onsta_station. x, data = dbs) Residuals: • Min 1 Q Median 3 Q Max • -2457. 97 -544. 91 -5. 51 582. 03 2793. 09 • Coefficients: • Estimate Std. Error t value Pr(>|t|) • (Intercept) 2457. 974 9. 471 259. 5 <2 e-16 *** • onsta_station. x 2 -685. 058 14. 421 -47. 5 <2 e-16 *** • onsta_station. x 4 -2204. 463 14. 279 -154. 4 <2 e-16 *** • onsta_station. x 5 -1233. 113 20. 622 -59. 8 <2 e-16 *** • onsta_station. x 6 -1323. 068 12. 867 -102. 8 <2 e-16 *** • -- • Signif. codes: 0 ‘***’ 0. 001 ‘**’ 0. 01 ‘*’ 0. 05 ‘. ’ 0. 1 ‘ ’ 1 • Residual standard error: 804. 8 on 28829 degrees of freedom • Multiple R-squared: 0. 4758, Adjusted R-squared: 0. 4757

3. Anova effect of stations • aov 2 <- aov(bweight_weight ~ onsta_station. x, data = dbs)> summary(aov 2) • Df Sum Sq Mean Sq F value Pr(>F) • onsta_station. x 1 8. 565 e+09 9128 <2 e-16 *** • Residuals 28832 2. 705 e+10 9. 383 e+05 • -- • Signif. codes: 0 ‘***’ 0. 001 ‘**’ 0. 01 ‘*’ 0. 05 ‘. ’ 0. 1 ‘ ’ 1

4. ANOVA - effect of strain • dbs$breg_strain <- as. factor(dbs$breg_strain) • > aov 3 <- aov(bweight_weight ~ breg_strain, data = dbs)> summary(aov 3) • Df Sum Sq Mean Sq F value Pr(>F) • breg_strain 6 5. 343 e+09 890450143 847. 9 <2 e-16 *** • Residuals 28827 3. 027 e+10 1050221

5. Interaction effect of strain by pen • aov 4 <- aov(bweight_weight ~ onsta_station. x + breg_strain/onsta_pen , data = dbs) • > summary(aov 4) • Df Sum Sq Mean Sq F value Pr(>F) • onsta_station. x 4 1. 695 e+10 4. 236 e+09 9150. 2 <2 e-16 *** • breg_strain 6 8. 105 e+08 1. 351 e+08 291. 8 <2 e-16 *** • breg_strain: onsta_pen 75 4. 551 e+09 6. 068 e+07 131. 1 <2 e-16 *** • Residuals 28748 1. 331 e+10 4. 630 e+05 • -- • Signif. codes: 0 ‘***’ 0. 001 ‘**’ 0. 01 ‘*’ 0. 05 ‘. ’ 0. 1 ‘ ’ 1

Mean comparison • Fit: aov(formula = bweight_weight ~ breg_strain, data = dbs) $breg_strain • diff lwr upr p adj • 2 -1 -550. 42310 -616. 523967 -484. 32224 0. 0000000 • 4 -1 36. 05591 -9. 355157 81. 46699 0. 2244597 • 7 -1 -1518. 70219 -1608. 000273 -1429. 40410 0. 0000000 • 8 -1 -807. 95837 -866. 005410 -749. 91132 0. 0000000 • 9 -1 -539. 24947 -605. 627927 -472. 87101 0. 0000000 • 10 -1 -1070. 08659 -1219. 994190 -920. 17900 0. 0000000 • 4 -2 586. 47902 520. 549248 652. 40879 0. 0000000 • 7 -2 -968. 27909 -1069. 564410 -866. 99376 0. 0000000 • 8 -2 -257. 53526 -332. 728537 -182. 34199 0. 0000000 • 9 -2 11. 17363 -70. 622874 92. 97014 0. 9996717 • 10 -2 -519. 66349 -677. 006607 -362. 32037 0. 0000000

Mean comparison continued • • • 7 -4 -1554. 75810 -1643. 929615 -1465. 58659 0. 0000000 8 -4 -844. 01428 -901. 866418 -786. 16215 0. 0000000 9 -4 -575. 30538 -641. 513466 -509. 09730 0. 0000000 10 -4 -1106. 14251 -1255. 974741 -956. 31028 0. 0000000 8 -7 710. 74382 614. 520892 806. 96675 0. 0000000 9 -7 979. 45272 877. 986013 1080. 91943 0. 0000000 10 -7 448. 61560 280. 206331 617. 02486 0. 0000000 9 -8 268. 70890 193. 271481 344. 14631 0. 0000000 10 -8 -262. 12823 -416. 261259 -107. 99519 0. 0000110 10 -9 -530. 83712 -688. 297061 -373. 37719 0. 0000000