Desempenho reprodutivo e taxa de retenção de matrizes suínas de acordo com peso e idade a primeira inseminação

O objetivo do presente estudo foi avaliar diferentes classe de idade e peso corporal na primeira IA sobre o desempenho reprodutivo e taxa de retenção até o terceiro parto de matrizes suínas. No total, 1184 leitoas da raça Landrace x Large White foram selecionadas aos 110 dias de gestação e inicialmente divididas em um modelo fatorial 3 × 3: três grupos de idade: ≤ 216 d (n=388), 217-229 d (n=416) e ≥ 230 d (n=380), e três grupos de peso corporal: ≤ 158 kg (n=399), 159-168 kg (n=376) e ≥ 169 kg (n=409). Na sequência, foram submetidas a avaliação de caliper no pré-parto e no desmame. Não se observou efeito da interação classe de idade e peso na primeira inseminação sobre os parâmetros avaliados (P > 0,05). A idade das leitoas na IA teve pouca influência sobre parâmetros de desempenho e longevidade. Somente as leitoas com ≤ 216 d tiveram um maior percentual cumulativo de natimortos (NM) em relação as idades 217-229 d e ≥ 230 d (6,3; 5,6 e 5,5 %, respectivamente; P 0,05). Leitoas com ≤ 158 kg tiveram menos NM (P = 0,02) do que leitoas com 159-168 kg e ≥ 169 kg ao primeiro parto. A taxa de parto no segundo ciclo foi menor (P = 0,02) para as fêmeas com ≥ 169 kg (83,0 %) comparada com ≤ 158 kg (88,7 %) e 159-168 kg (90,2 %), mas não diferiram entre si no terceiro ciclo (P > 0,05). No segundo e terceiro parto os grupos foram semelhantes para NT, NV, NM e desmamados (P > 0,05). Não houve efeito da idade ou peso na primeira IA sobre as mudanças de caliper na primeira lactação (P > 0,05). Quando avaliado fêmeas que perderam ≤ 3 (n=444), perderam 2-1 (n=368), ou as que ganharam ≥ 0 (n=185) unidades de caliper, não foi observado efeitos sobre taxa de parto, NT e NV no ciclo subsequente (P > 0,05), apenas o intervalo desmame-concepção foi maior para fêmeas que perderam ≥ 3 comparado às que perderam 2-1 e ganharam ≥ 0 unidades de caliper (12,1; 10,4 e 8,0 dias, respectivamente; P 0,05). Assim, idade a primeira IA não influenciou a produtividade futura das fêmeas, não justificando sua utilização como critério para o momento da primeira IA. Porém, o peso corporal na primeira inseminação influenciou significativamente o desempenho reprodutivo subsequente, visto que leitoas pesadas na inseminação apresentaram maior percentual de NM, menor longevidade, enquanto que os NT e NV cumulativos não diferiram. A perda de escore de caliper durante a lactação não foi relacionada à idade ou peso corporal na primeira inseminação e não afetou o desempenho reprodutivo subsequente.The present study aimed to evaluate the effect of body weight and age at the first AI on longevity and reproductive performance until the third parity of sows. In total, 1184 Landrace x Large white gilts were selected at 110 days of gestation and divided into a 3 x 3 factorial design: three ages at first mating (AFM): ≤ 216 days (n=388), 217 to 229 days (n=416), and ≥ 230 days (n=380), and three groups of bodyweights (BW): ≤ 158 kg (n=399), 159 to 168 kg (n=376), and ≥ 169 kg (n=409). Gilts also were submitted to caliper evaluation at pre-farrowing and at weaning. There were no significant effects (P > 0.05) of interaction between AFM and BW on the evaluated parameters. The age of the gilts in AI had a minimal influence on reproductive performance and longevity. Only gilts with ≤ 216 d had a higher stillborn rate (SB) cumulative compared to 217-229 d and ≥ 230 d (6.3; 5.6 and 5.5 %, respectively; P 0.05). Gilts of BW ≤ 158 kg had less SB (P = 0.02) than gilts with 159-168 kg and ≥ 169 kg in the first parity. The farrowing rate in the second parity was lower (P = 0.02) for gilts with ≥ 169 kg (83.0 %) compared to gilts with ≤ 158 kg (88.7 %) and 159-168 (90.2 %) but did not differ among groups in the third parity (P > 0.05). In the second and third parity, the BW groups were similar for TB, BA, SB, and WP (P > 0.05). There is no effect of AFM and BW on caliper change during the first lactation (P > 0.05). When evaluated sows that lost ≤ 3 (n=444), lost 2-1 (n= 368), or those who gained ≥ 0 (n=185) caliper units were not observed effect on farrowing rate, TB and BA in the second parity (P > 0.05), only the weaning-conception interval was longer for females that lost ≥ 3 compared to those that lost 2- 1 and gained ≥ 0 caliper units (12.1; 10.4 and 8.0 d, respectively; P 0.05). Thus, in our study AFM did not influence the future productivity of the female, not justifying its use as a criterion for the moment of the first AI. However, BW at the first mating significantly influenced subsequent reproductive performance, as heavy gilts at mating had a higher SB, shorter longevity, while the TB and BA cumulative were similar between the evaluated groups. The loss of caliper unit during lactation was not related to AFM or BW at first insemination and did not affect subsequent reproductive performance

Fatores de risco associados à natimortalidade em fêmeas suínas

In this study, the risk factors associated with stillbirth in sows were identified and their odds ratio assessed. For this purpose, 587 farrowings on Farm A and 929 on Farm B were monitored, and the sow parity, body condition score, farrowing duration, total number of piglets born, numbers of live births, stillbirths, and mummified piglets, obstetric interventions, and piglet sex and weight were recorded. At the end of farrowing, piglets classified as stillborn were necropsied to confirm the diagnosis. Consequently, 5.49% of the piglets on Farm A and 5.10% of those on Farm B were stillborn. On both farms, sows with a high parity, prolonged farrowing, and a large litter size had the highest odds ratio of stillbirths. On Farm B, farrowing intervention through the use of vaginal palpation and oxytocin increased the odds of stillbirth by 1.7 and 2.5 times, respectively. Heavy litters increased the odds of stillbirth by 1.4 times. Additionally, low-birth-weight piglets were 2.3 and 3.1 times more likely than their medium-birth-weight and high-birth-weight counterparts, respectively, to be stillborn. In conclusion, on both farms, the risk factors associated with stillbirth were a high parity, a large litter size, and prolonged farrowing.O objetivo deste estudo foi avaliar os fatores de risco associados ao nascimento de natimortos em fêmeas suínas e a razão de chance para a sua ocorrência. Foram acompanhados 587 partos na granja A e 929 na granja B onde foram registrados: ordem de parto, escore de condição corporal, duração do parto, total de nascidos, nascidos vivos, natimortos, mumificados, intervenções ao parto, peso e o sexo dos leitões. Ao final do parto foi realizada necropsia dos leitões classificados como natimortos a fim de confirmar o diagnóstico. A ocorrência de leitões natimortos foi de 5,49% e 5,10% na granja A e B, respectivamente. Fêmeas de maior ordem de parto, com partos prolongados e leitegadas mais numerosas apresentaram maior chance da ocorrência de leitões natimortos em ambas as granjas. Na granja B a necessidade de intervenção ao parto através do uso de ocitocina e palpação vaginal aumentaram a chance da presença de leitões natimortos em 1,7 e 2,5 vezes, respectivamente. Leitegadas pesadas aumentaram em 1,4 vezes as chances de ocorrência de leitões natimortos. No entanto, leitões com menor peso ao nascer aumentam em 2,3 vezes as chances de natimortos, quando comparadas a leitões de peso intermediário, e 3,1 vezes em relação a leitões com maior peso de nascimento. Os fatores de risco associados à ocorrência de natimortos nas duas granjas foram a ordem de parto elevada, leitegadas numerosas e partos prolongados

Proposal of Equations for Predicting Post-Farrowing Sow Weight

Background: Body condition score is used widely in swine production to ensure adequate nutritional levels in sows during gestation and lactation. However, body condition score is not a gold standard for the estimation of nutritional requirements in sows. Post-farrowing sow body weight assessment might serve as a useful approach for the better adjustment of the nutritional requirements during lactation; however, this approach is time-consuming, requires labor, and might result in detrimental effects on the sow behavior and welfare. The objective of the present study, therefore, was to formulate prediction equations for the estimation of post-farrowing sow weight.Materials, Methods & Results: Seven equations were formulated for predicting the post-farrowing sow body weight, by using the data from three databases, which comprised a total 522 sows (434 gilts and 88 multiparous). The sows were weighed on Day 112 of gestation and after farrowing within 12 h. The piglets birth weight was recorded within 24 h after farrowing. The equations were formulated considering all the parity orders. While formulating the equations, the following five variables were used: pre-farrowing body weight, piglets born, litter weight, the interval between pre-farrowing weighing and farrowing (in days), and the total feed intake between pre-farrowing and post-farrowing weighing. The seven models were compared using the sets of possible predictors through regression with the best subsets procedure (Minitab for Windows, v. 18). Equations (EQ) 1, 2, and 4 were validated with a database comprising 732 sows (parity orders: 1–5). The females were weighed on Day 107 of gestation and within 24 h after farrowing. The predicted weights estimated by EQ 2 and 4 (215.4 ± 34.3 kg and 216.7 ± 34.4 kg, respectively) did not significantly differ from the observed weight (216.8 ± 34.6 kg) [P > 0.05].Discussion: Pre-farrowing sow body weight was identified as the main input variable required for the estimation of the post-farrowing sow body weight. Thus, even EQ 1, which contained only this variable, exhibited a high coefficient of determination (R2 = 0.8707). However, the R2 value kept increasing as more input variables were included in the equation. Equation 2, 4, and 6 included the litter weight variable, and the addition of this variable increased the numerical value of R2 from 0.8707 in EQ 1 to 0.8975 in EQ 2. The EQ 3, 5, and 7 considered the piglets born variable as well, which increased the R2 value from 0.8707 in EQ 1 to 0.9119 in EQ 3. The coefficient of determination did not vary much among the equations; therefore, the selection of the prediction equations depended on data availability, feed management, facility, and the reliability of data collection in each farm. Although EQ 1 demonstrated a greater correlation between the predicted and the observed post-farrowing weight compared to the other equations, the values of error in central tendency and the errors due to disturbances were numerically higher for EQ 1 in comparison to the other two equations (EQ 2 and 4). Therefore, it is suggested that EQ 1 should be used as the last choice for the estimation of post-farrowing sow weight as it presented low trueness and precision, and also because the predicted weight estimated by EQ 1 was statistically lower than the observed weight (211.67 ± 33.33 kg vs. 216.84 ± 34.62 kg; P = 0.012). EQ 4 emonstrated higher trueness and precision; however, it did not differ significantly from EQ 2 and 1. Further analyses are required in order to validate EQ 3, 5, 6, and 7. Among the equations that were predicted as well as validated, the simplest and the easiest equation with satisfactory results for trueness and precision was EQ 2, which is as follows:Post-farrowing sow weight (kg) = 13.03 + (0.93 × pre-farrowing body weight, kg) + (–1.23 × piglets born, n

Proposal of equations for predicting post-farrowing sow weight

Background: Body condition score is used widely in swine production to ensure adequate nutritional levels in sows during gestation and lactation. However, body condition score is not a gold standard for the estimation of nutritional requirements in sows. Post-farrowing sow body weight assessment might serve as a useful approach for the better adjustment of the nutritional requirements during lactation; however, this approach is time-consuming, requires labor, and might result in detrimental effects on the sow behavior and welfare. The objective of the present study, therefore, was to formulate prediction equations for the estimation of post-farrowing sow weight. Materials, Methods & Results: Seven equations were formulated for predicting the post-farrowing sow body weight, by using the data from three databases, which comprised a total 522 sows (434 gilts and 88 multiparous). The sows were weighed on Day 112 of gestation and after farrowing within 12 h. The piglets birth weight was recorded within 24 h after farrowing. The equations were formulated considering all the parity orders. While formulating the equations, the following five variables were used: pre-farrowing body weight, piglets born, litter weight, the interval between pre-farrowing weighing and farrowing (in days), and the total feed intake between pre-farrowing and post-farrowing weighing. The seven models were compared using the sets of possible predictors through regression with the best subsets procedure (Minitab for Windows, v. 18). Equations (EQ) 1, 2, and 4 were validated with a database comprising 732 sows (parity orders: 1-5). The females were weighed on Day 107 of gestation and within 24 h after farrowing. The predicted weights estimated by EQ 2 and 4 (215.4 ± 34.3 kg and 216.7 ± 34.4 kg, respectively) did not significantly differ from the observed weight (216.8 ± 34.6 kg) [P > 0.05]. Discussion: Pre-farrowing sow body weight was identified as the main input variable required for the estimation of the post-farrowing sow body weight. Thus, even EQ 1, which contained only this variable, exhibited a high coefficient of determination (R2 = 0.8707). However, the R2 value kept increasing as more input variables were included in the equation. Equation 2, 4, and 6 included the litter weight variable, and the addition of this variable increased the numerical value of R2 from 0.8707 in EQ 1 to 0.8975 in EQ 2. The EQ 3, 5, and 7 considered the piglets born variable as well, which increased the R2 value from 0.8707 in EQ 1 to 0.9119 in EQ 3. The coefficient of determination did not vary much among the equations; therefore, the selection of the prediction equations depended on data availability, feed management, facility, and the reliability of data collection in each farm. Although EQ 1 demonstrated a greater correlation between the predicted and the observed post-farrowing weight compared to the other equations, the values of error in central tendency and the errors due to disturbances were numerically higher for EQ 1 in comparison to the other two equations (EQ 2 and 4). Therefore, it is suggested that EQ 1 should be used as the last choice for the estimation of post-farrowing sow weight as it presented low trueness and precision, and also because the predicted weight estimated by EQ 1 was statistically lower than the observed weight (211.67 ± 33.33 kg vs. 216.84 ± 34.62 kg; P = 0.012). EQ 4 demonstrated higher trueness and precision; however, it did not differ significantly from EQ 2 and 1. Further analyses are required in order to validate EQ 3, 5, 6, and 7. Among the equations that were predicted as well as validated, the simplest and the easiest equation with satisfactory results for trueness and precision was EQ 2, which is as follows: Post-farrowing sow weight (kg) = 13.03 + (0.93 × pre-farrowing body weight, kg) + (-1.23 × piglets born, n