Modelagem do peso pós-parto de primíparas suínas

A produtividade da fêmea suína aumentou de forma substancial nos últimos anos devido ao manejo, avanços genéticos e seleção. Porém, o peso médio dos leitões ao nascimento diminui, com consequente aumento da mortalidade durante a fase de maternidade. Algumas estratégias são fundamentais para minimizar o problema. Entre as principais, devemos enfatizar as estratégias de alimentação e nutrição durante a fase de gestação, pois as quantidades de nutrientes exigidos pelas fêmeas e seus conceptos devem ser corretamente atendidas. Para calcular de forma precisa a quantidade de nutrientes a serem fornecidos para a fêmea durante a fase de gestação, é fundamental ter o conhecimento do peso materno após o parto, o que permite estimar seu ganho de peso durante a gestação e o ganho dos seus conceptos. Assim, o objetivo do presente estudo foi desenvolver um modelo que possa prever o peso materno pósparto. Foram utilizadas 141 fêmeas primíparas, sendo pesadas na transferência do galpão gestação para o galpão maternidade e pesadas novamente em até 12 horas pós-parto. Entre o período que ocorreu a transferência e a pesagem pós-parto, foi acompanhado o consumo de ração individual de cada animal. Antes de cada arraçoamento foi realizada uma avaliação do consumo da fêmea, anotando sobras do último fornecimento de ração. Os leitões nascidos vivos e natimortos foram pesados em até 12 horas pós-parto. Os leitões mumificados foram identificados e registrados. O procedimento ‘Best Subject’ do software Minitab foi utilizado para escolha dos modelos analíticos. Após, as equações foram definidas utilizando o procedimento ‘General Linear Model’. Os termos que foram significativos (P < 0,05) e que geraram os modelos com maiores coeficientes de determinação foram: peso na transferência, peso da leitegada, número total de leitões e intervalo entre a transferência e o parto. O termo ‘peso na transferência’ foi identificado como o principal fator para estimar o peso pós-parto e, por isso, foi considerado em todas as equações propostas. Os termos ‘peso da leitegada’ ou ‘número total de leitões’ foram utilizados separadamente, pois são variáveis correlacionadas entre si (r = 0,77; P < 0,01). Neste contexto, a equação que melhor estimou o peso pós-parto de uma fêmea suína e que possui maior aplicabilidade prática é descrita da seguinte maneira: y = 19,36 + (0,8432 ×b1) + (-1,302 ×b2) + (2,185 ×b3). Sendo: y, peso da fêmea pós-parto, expresso em kg; b1, peso pré-parto, expresso em kg; b2, total de leitões nascidos, n; b3, intervalo entre a transferência e o parto, expresso em dias. A equação gerada possui um coeficiente de determinação satisfatório (r2 = 0,81). É possível estimar o peso pós-parto de fêmeas suínas com boa confiabilidade. As equações de previsão são ferramentas que podem se tornar parte importante na rotina dos profissionais da área.The productivity of the sow has increased substantially in recent years due to management, genetic advances and selection. However, the mean weight of piglets at birth decreases, with consequent increase in mortality during the farrowing phase. Some strategies are critical to minimizing the problem. Among the main ones, we should emphasize feeding and nutrition strategies during the gestation phase, since the amount of nutrients required by sows and their concepts must be correctly supplied. To accurately calculate the amount of nutrients to be supplied to the sow during the gestation phase, it is essential to have the knowledge of the maternal weight after farrowing, which allows estimating her weight gain during gestation and the gain of her concepts. Thus, the objective of the present study was to develop a model that can predict postpartum maternal weight. We used 141 primiparous females, being weighed in the transfer of the gestation shed to the farrowing shed and weighed again within 12 hours postpartum. Within the time the transfer occurred and the postpartum weighing, the consumption of individual rations of each animal was monitored. Before each feeding, an evaluation of the consumption of the female was made, noting leftovers from the last feed supply. Liveborn and stillborn piglets were weighed within 12 hours postpartum. The mummified piglets were identified and recorded. The 'Best Subject' procedure of the Minitab software was used to choose the analytical models. After, the equations were defined using the 'General Linear Model' procedure. The terms that were significant (P <0.05) and generated the models with the highest coefficients of determination were: transfer weight, litter weight, total number of piglets and interval between transfer and farrowing. The term 'transfer weight' was identified as the main factor for estimating postpartum weight and therefore was considered in all proposed equations. The terms 'litter weight' or 'total number of piglets' were used separately, since they are correlated variables (r = 0.77, P <0.01). In this context, the equation that best estimated the postpartum weight of a female pig and which has a greater practical applicability is described as follows: y = 19,36 + (0,8432 × b1) + (-1,302 × b2) + (2,185 × b3). Where: y, postpartum female weight, expressed in kg; B1, pre-farrowing weight, expressed in kg; B2, total of born piglets, n; B3, interval between transfer and farrowing, expressed in days. The equation generated has a satisfactory coefficient of determination (r2 = 0.81). It is possible to estimate the postpartum weight of swine females with good reliability. Prediction equations are tools that can become an important part of the routine of professionals in the field

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 &amp; 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 &gt; 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

Modelagem do peso pós-parto de primíparas suínas

A produtividade da fêmea suína aumentou de forma substancial nos últimos anos devido ao manejo, avanços genéticos e seleção. Porém, o peso médio dos leitões ao nascimento diminui, com consequente aumento da mortalidade durante a fase de maternidade. Algumas estratégias são fundamentais para minimizar o problema. Entre as principais, devemos enfatizar as estratégias de alimentação e nutrição durante a fase de gestação, pois as quantidades de nutrientes exigidos pelas fêmeas e seus conceptos devem ser corretamente atendidas. Para calcular de forma precisa a quantidade de nutrientes a serem fornecidos para a fêmea durante a fase de gestação, é fundamental ter o conhecimento do peso materno após o parto, o que permite estimar seu ganho de peso durante a gestação e o ganho dos seus conceptos. Assim, o objetivo do presente estudo foi desenvolver um modelo que possa prever o peso materno pósparto. Foram utilizadas 141 fêmeas primíparas, sendo pesadas na transferência do galpão gestação para o galpão maternidade e pesadas novamente em até 12 horas pós-parto. Entre o período que ocorreu a transferência e a pesagem pós-parto, foi acompanhado o consumo de ração individual de cada animal. Antes de cada arraçoamento foi realizada uma avaliação do consumo da fêmea, anotando sobras do último fornecimento de ração. Os leitões nascidos vivos e natimortos foram pesados em até 12 horas pós-parto. Os leitões mumificados foram identificados e registrados. O procedimento ‘Best Subject’ do software Minitab foi utilizado para escolha dos modelos analíticos. Após, as equações foram definidas utilizando o procedimento ‘General Linear Model’. Os termos que foram significativos (P < 0,05) e que geraram os modelos com maiores coeficientes de determinação foram: peso na transferência, peso da leitegada, número total de leitões e intervalo entre a transferência e o parto. O termo ‘peso na transferência’ foi identificado como o principal fator para estimar o peso pós-parto e, por isso, foi considerado em todas as equações propostas. Os termos ‘peso da leitegada’ ou ‘número total de leitões’ foram utilizados separadamente, pois são variáveis correlacionadas entre si (r = 0,77; P < 0,01). Neste contexto, a equação que melhor estimou o peso pós-parto de uma fêmea suína e que possui maior aplicabilidade prática é descrita da seguinte maneira: y = 19,36 + (0,8432 ×b1) + (-1,302 ×b2) + (2,185 ×b3). Sendo: y, peso da fêmea pós-parto, expresso em kg; b1, peso pré-parto, expresso em kg; b2, total de leitões nascidos, n; b3, intervalo entre a transferência e o parto, expresso em dias. A equação gerada possui um coeficiente de determinação satisfatório (r2 = 0,81). É possível estimar o peso pós-parto de fêmeas suínas com boa confiabilidade. As equações de previsão são ferramentas que podem se tornar parte importante na rotina dos profissionais da área.The productivity of the sow has increased substantially in recent years due to management, genetic advances and selection. However, the mean weight of piglets at birth decreases, with consequent increase in mortality during the farrowing phase. Some strategies are critical to minimizing the problem. Among the main ones, we should emphasize feeding and nutrition strategies during the gestation phase, since the amount of nutrients required by sows and their concepts must be correctly supplied. To accurately calculate the amount of nutrients to be supplied to the sow during the gestation phase, it is essential to have the knowledge of the maternal weight after farrowing, which allows estimating her weight gain during gestation and the gain of her concepts. Thus, the objective of the present study was to develop a model that can predict postpartum maternal weight. We used 141 primiparous females, being weighed in the transfer of the gestation shed to the farrowing shed and weighed again within 12 hours postpartum. Within the time the transfer occurred and the postpartum weighing, the consumption of individual rations of each animal was monitored. Before each feeding, an evaluation of the consumption of the female was made, noting leftovers from the last feed supply. Liveborn and stillborn piglets were weighed within 12 hours postpartum. The mummified piglets were identified and recorded. The 'Best Subject' procedure of the Minitab software was used to choose the analytical models. After, the equations were defined using the 'General Linear Model' procedure. The terms that were significant (P <0.05) and generated the models with the highest coefficients of determination were: transfer weight, litter weight, total number of piglets and interval between transfer and farrowing. The term 'transfer weight' was identified as the main factor for estimating postpartum weight and therefore was considered in all proposed equations. The terms 'litter weight' or 'total number of piglets' were used separately, since they are correlated variables (r = 0.77, P <0.01). In this context, the equation that best estimated the postpartum weight of a female pig and which has a greater practical applicability is described as follows: y = 19,36 + (0,8432 × b1) + (-1,302 × b2) + (2,185 × b3). Where: y, postpartum female weight, expressed in kg; B1, pre-farrowing weight, expressed in kg; B2, total of born piglets, n; B3, interval between transfer and farrowing, expressed in days. The equation generated has a satisfactory coefficient of determination (r2 = 0.81). It is possible to estimate the postpartum weight of swine females with good reliability. Prediction equations are tools that can become an important part of the routine of professionals in the field

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

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

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 &amp; 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 &gt; 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