Proposal of Equations for Predicting Post-Farrowing Sow Weight

Abstract

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)