The impact of herd structure on the performance of commercial sow-breeding farms

The impact of herd structure on the performance of commercial sow-breeding farms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The impact of herd structure on the performance of commercial sow-breeding farms Santos Sanz-Fernández, Cipriano Díaz-Gaona, João Simões, José Carlos Casas-Rosal, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4504842/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Nov, 2024 Read the published version in Porcine Health Management → Version 1 posted 9 You are reading this latest preprint version Abstract Background The herd structure, i.e., distribution of sows within a farm based on their parity number, and its management are essential to optimise farm reproductive efficiency. The objective of this study is to define different types of herd structure using data from 623 Spanish commercial sow farms. Additionally, this study aims to determine which type of herd structure can enhance reproductive efficiency at the farm level. Results Farms are classified into three groups according to the quadratic function fitted to the percentage of sows over parities. This classification unveils three types of herd structures: type 1 (HS1) exhibits a concave-downward trend, with a higher percentage of sows in intermediate parities (mean of 45.5% sows between the 3rd to 5th parity); type 2 (HS2) presents a trend curve that is close to a straight line, with a gradual decrease in the percentage of sows per parity (approximately 2% loss of sows census per parity); and type 3 (HS3) shows an upward concave trend curve, with an increase in the percentage of sows in later parities (19.0% of sows between 7th and ≥ 8th parity). Additionally, parametric tests (ANOVA followed by the Tukey HSD test) assess productivity differences between the three groups of farms with different herd structures. Significant differences (p < 0.01) are noted in number of piglets weaned per sow per year, farrowing rate, percentage of sows returning to oestrus and number of weaned piglets, with a medium effect size (values of η 2 between 0.06 to < 0.14). Farms with HS1 (showing a concave-downward trend) have the best productive outcomes over a year, surpassing the results of farms with HS2 and even more so those of HS3 farms. Conclusions This study shows the importance of herd structure on sow-breeding farms as factor of reproductive efficiency. The results endorse the proposed classification based on the curvature of the trend parabola obtained with the quadratic function to categorize herd structures into three groups. Besides that, these highlight the importance of considering the herd structure in farm decision-making. replacement rate census structure parity breeding sows reproductive performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Background One crucial aspect for achieving optimal farm efficiency is to control the herd structure of breeding sows [ 1 , 2 ], because it directly influences the number of piglets produced within a herd [ 3 ]. The herd structure refers to the distribution of sows within a farm based on the number of reproductive cycles or parities. In general, the herd structure can be divided into three main groups of sows [ 4 ]: gilts and primiparous sows, which exhibit lower prolificacy and fewer weaned piglets; mid-parity sows (third to fifth parity), with the highest productivity; and old sows, encompassing sows from the sixth to eighth parity or older, which are those close to being culled and have physiological traits that decrease their productivity. In addition, piglets survival rates also vary across sow reproductive cycles [ 5 ] due to variations in colostrum and milk production, immunoglobulin concentration, and birth weight within litters [ 6 – 8 ]. Therefore, to ensure optimum farm performance, it is crucial to study the distribution of sows across parities (herd structure), as well as to consider other reproductive parameters such as the number of piglets weaned per sow and year (PWSY), the number of farrowings per sow per year, and the sow lifetime performance [ 9 ]. Thus, the proportion of sows within each parity group, determined by sow removal and by the maximum number of productive parities at which sows are culled, is a crucial factor in the functionality, productivity and profitability of a pig farm. Carroll [ 10 ] defined the ideal herd structure as one that has a gradually decreasing percentage of sows from 1st to 8th parity, which has been widely accepted as the best one for ensuring farm efficiency. This structure suggests a 1st parity sow percentage of ≥ 17% and a maximum 8th parity sow percentage of ≤ 4%. More recently, several authors have also recommended maintaining the percentage of 1st parity sows between 15–20% breeding sows [ 1 , 3 , 11 – 13 ]. In this same line, Koketsu [ 4 ] recommended maintaining a stable census, with stable subpopulations of mid-parity sows and mated gilts to optimise herd productivity. Nevertheless, many farms have unstructured sow herd distribution derived from challenges in sow culling or replacement program; which results in worse productivity parameters. Despite evidence, the impact of herd structure on farm productivity has barely been analysed. The objective of this study is to define different types of herd structures studying 623 Spanish commercial sow farms to assess the relationship between herd structure and farm performance, and to determine which type of herd structure can enhance pig farm reproductive efficiency. Material and methods Data source The dataset analysed in the present study comes from the BDporc® databank [ 14 ] within the framework of a collaboration agreement between the Institute of Agrifood Research and Technology (IRTA) and the Department of Animal Production of the University of Cordoba. The BDporc® is the main database of sow-breeding farms in Spain, where productive data are collected, through the periodic submission of data generated on the farms, gathered in their own software’s for data collection and management. The analysed dataset was collected in 2020 from 623 commercial farms, with a census of 888,479 reproductive sows, representing approximately 40% of the sow-breeding census in Spain. For confidentiality reasons, there is no information available on the genetics of the sows from the studied farms. Nevertheless, these sows are derived from modern commercial lines. Farms with Iberian breed sows were not included. Therefore, the results of this study can be extrapolated beyond Spain, the largest pork producer in Europe and the third globally [ 15 ]. Regarding the farms included in the study, these should have at least records until the 6th parity, in order to evaluate their census structure. A total of 8 farms were excluded from the study for not having data up to 6th parity. Since most of the sows do not reach 8 or more parities and for the shake of simplification, BDporc groups sows with 8 or more parities into a single group. Similarly, these oldest parities are grouped in other studies of intensive farms [ 4 ] because those sows are close to be culled [ 16 , 17 ]. The productive parameters analysed at the farm level have correspond to the data collected over one year, from January to December 2020, and are annual farm averages: number of sows on the farm; replacement rate (proportion of sows newly introduced into the farm relative to the average number of sows present); piglets weaned per sow per year (PWSY); age of sows at culling (months); farrowings per culled sow (average of total farrowings performed by sows until their culling); total number of piglets weaned per culled sow (total number of piglets produced by a sow throughout her life, until its culling); farrowings per sow and year; farrowing rate; percentage of sow return to oestrus; weaning-to-first-service interval (WSI); weaning-to-oestrus interval (WOI); weaning to conception interval (WCI); number of total born (TB), born alive (BA), stillborn (SB) and weaned (W) piglets; mortality rate of TB piglets at weaning; and mortality rate of BA piglets at weaning. To analyse the census structure, the percentage of sows within each parity has been calculated, using the total number of litters recorded for each parity over a year (N = 1,860,663 litters). Additionally, the average number of BA and W piglets as well as WCI are analysed by parity. Modelling herd structure and classification of farms into groups The farms have been classified into three groups based on the quadratic function of the herd structure of each farm, where the dependent variable is the percentage or proportion of sows, and the independent variable is the parity or cycle of the sow (1st to ≥ 8th parity). This function was selected because it is easy-to-interpret and provides a good fit to the real distribution of sow census across parities within farms. Additionally, it enables the assessment of non-linear relationships between the parity number and the sow census, offering a high degree of flexibility to accurately capture complex patterns and variations in the data. To compare the goodness of fit of the different functional forms, the Akaike information criterion has been used. The graphical representation of a quadratic function is a parabola, defined by the equation: f(x) = ax 2 + bx + c. The coefficients of the quadratic regression provide information about the shape of the curve and the relationship between variables. Thus, the coefficient "a" determines the orientation of the graph, indicating the curvature of the function and whether the parabola opens upwards or downwards [ 18 ]. Moreover, the absolute value of "a" determines the magnitude of the parabola's curvature and its direction. This “a” coefficient was calculated by least squares method for quadratic functions using the editor of Python V.3.10.10, Pyzo V.4.12.8. Accordingly, the three groups of herd structure are based on the value of the coefficient "a", classifying the farms according to the 25th, 50th, and 75th percentiles (extreme values and median) of this coefficient; these three groups of herd structures are: type 1 (HS1), corresponding to the percentile 25, with the lowest value of the coefficient "a" (negative values; N = 156 farms); type 2 (HS2), corresponding to the percentile 50 of the coefficient "a" (values closest to zero; N = 311 farms); type 3 (HS3), corresponding to the percentile 75, with the highest value of the coefficient "a" (positive values; N = 156 farms). For each group of herd structure, its quadratic function has been calculated, obtaining the coefficient of determination (R 2 ) and the Root Mean Square Error (RMSE), Additionally, the linear function of each herd structure group has been calculated in parallel to compare and validate the fit of the herd structure to a quadratic model. Statistical analysis Statistical analyses of the dataset were performed using IBM SPSS ® 22 software. Descriptive statistics were calculated for the productive parameters of all farms and for each parity, as well as for sow distribution. To compare the three types of herd structure, parametric tests were conducted after assessing normality using skewness and kurtosis calculations; to meet normality criteria established by Cohen [ 19 ], skewness and kurtosis values should range from − 3 to 3 and between − 8 and 8, respectively. Specifically, the ANOVA test was performed, followed by the Tukey HSD test to analyse the differences in distribution between the three types of herd structure compared to their productive outcomes. Furthermore, for cases where significant differences were observed, the effect size (η 2 ) was calculated to measure the magnitude of the differences found. The effect size of an ANOVA is the value that measures how much the independent variable or factor (the type of herd structure) affects the dependent variable (the productive parameters). Cohen (1988) provides classification benchmarks for effect size levels, defining small effects (η 2 = 0.01 to < 0.06), medium effects (η 2 = 0.06 to < 0.14), and large effects (η 2 ≥ 0.14). Results Evaluation of the productive parameters of the farms Descriptive statistics of the productive variables associated with the performance of the studied farms are shown in Table 1 . In general, the census of the studied farms exceeded 1400 breeding sows, with a mean annual productivity of 29.73 PWSY and a mean of 2.43 farrowings per sow per year. Table 1 Descriptive statistics for the productive factors of the commercial farms under study (N = 623). Mean Standard deviation Percentiles 25 50 75 Mean number of sows on the farm 1426.13 1217.06 561.64 983.11 2057.64 Replacement rate (%) 47.31 13.10 40.19 45.78 52.76 Number of piglets weaned per sow per year 29.73 3.44 27.47 29.47 32.04 Culled sow age (months) 32.75 4.61 30.10 32.62 35.11 Farrowings per culled sow 4.55 0.90 4.05 4.57 5.06 Total number of piglets weaned per culled sows 54.73 11.99 47.66 54.80 61.50 Farrowings per sow and year 2.43 0.08 2.40 2.44 2.48 Farrowing rate (%) 84.62 5.74 81.54 85.34 88.29 Percentage of sows return to oestrus 13.95 5.58 10.27 13.02 17.13 Weaning-to-first-service interval (WSI, days) 6.17 1.75 5.20 5.72 6.58 Weaning-to-oestrus interval (WOI, days) 4.90 0.73 4.49 4.80 5.15 Weaning to conception interval (WCI, days) 9.37 3.40 7.25 8.65 10.48 Number of piglets total born (TB, days) 15.63 2.01 14.16 15.08 17.33 Number of piglets born alive (BA, days) 14.30 1.71 13.06 13.89 15.79 Number of piglets still born per litter 1.33 0.48 1.02 1.27 1.59 Number of piglets weaned per litter 12.22 1.36 11.21 12.06 13.15 Mortality rate of BA piglets at weaning (%) 14.41 4.53 11.57 14.08 17.33 Mortality rate of TB piglets at weaning (%) 18.41 6.97 12.58 19.10 23.55 In terms of sow longevity, the average age of culled sows was approximately 33 months, with a mean of 4.55 farrowings and 54.73 piglets weaned per sow lifetime. These farms had a mean replacement rate of 47.31%, with a mean percentage of sows returning to oestrus of nearly 14%, and a mean WCI of 9.37 days. Figure 1 shows the descriptive statistics for sow distribution, prolificacy and WCI per parity for the total number of farms. Thus, the herd structure of all farms shows a gradual decrease in the percentage of sows from the 1st parity (with a mean of 19.58% sows) to the 7th parity (with a mean of 7.18% sows), approximately maintaining this census in the ≥ 8th parities. Regarding litter size, the highest prolificacy for BA piglets was achieved at the 3rd and 4th parities, with means of 14.91 and 14.94 piglets, respectively; while the 2nd parity had the highest number of W piglets (12.51 piglets). On the other hand, the WCI gradually decreased as the number of parities increases, with a mean difference of nearly 6 days between the 2nd and ≥ 8th parities. Types of herd structure The farms were classified into three groups of herd structure (HS1, HS2 and HS3); as stated above this was determined by the coefficient “a” of the quadratic function fitted to the distribution of sows by parity (Figs. 2 , 3 and 4 ). Table 2 shows the percentage of sows at each parity (mean and median) and the prolificacy per parity for these three groups of farms. Table 2 Descriptive statistics for the sow distribution and prolificacy for piglets born alive and weaned at each parity according to the groups of herd structures (N = 623). Parity 1 Parity 2 Parity 3 Parity 4 Parity 5 Parity 6 Parity 7 Parity ≥ 8 Herd Structure Type 1 (a25%) (N = 156) Percentage of sows (mean) 17.55 16.57 16.25 15.44 13.85 10.99 6.80 2.56 Accumulative percentage of sows - 34.12 50.36 65.80 79.65 90.64 97.44 100.00 Percentage of sows (median) 17.73 16.78 15.93 14.74 13.60 11.12 7.16 1.98 Number of piglets born alive 14.24 14.69 15.35 15.31 15.00 14.59 14.12 13.71 Number of piglets weaned 12.94 13.06 12.90 12.79 12.57 12.41 12.37 12.32 Herd Structure Type 2 (a50%) (N = 311) Percentage of sows (mean) 19.30 17.03 15.06 13.21 11.37 9.45 7.47 7.11 Accumulative percentage of sows - 36.33 51.39 64.61 75.97 85.42 92.89 100.00 Percentage of sows (median) 19.22 17.00 15.03 13.21 11.37 9.45 7.47 7.11 Number of piglets born alive 13.70 14.21 14.81 14.85 14.59 14.18 13.69 13.16 Number of piglets weaned 12.34 12.40 12.28 12.14 11.98 11.79 11.63 11.53 Herd Structure Type 3 (a75%) (N = 156) Percentage of sows (mean) 22.17 17.42 13.45 10.78 9.05 8.12 7.00 12.01 Accumulative percentage of sows - 39.58 53.04 63.82 72.87 80.99 87.99 100.00 Percentage of sows (median) 21.40 16.91 13.43 10.95 9.34 8.15 6.87 12.13 Number of piglets born alive 13.57 14.12 14.66 14.76 14.47 14.11 13.57 12.86 Number of piglets weaned 12.01 12.17 12.03 11.99 11.88 11.81 11.70 11.37 HS1 is characterised by maintaining a higher percentage of sows in the intermediate parities (an average of 45.5% sows between the 3rd to 5th parity; Table 2 ). The obtained quadratic function for this group shows a concave-downward trend curve (Fig. 2 ). HS1 farms had negative coefficient "a" (ranging from − 1.8509 to -0.1346). The quadratic function representing the herd structure is as follows: f(x) = -0.38x 2 + 1.39x + 15.95, with a coefficient of determination R 2 = 0.76 and RMSE = 2.83 (p < 0.001). The coefficient of determination R 2 indicates that approximately 76% of the variation in the herd structure within this group of farms can be explained by the quadratic function, and the predicted values by the quadratic function differ from the actual values by approximately 2.83 units on average, indicating a good fit of the model. HS2 is characterised by a trend curve that is close to a straight line, with a gradual decrease in the percentage of sows from 1st to 8th parity, resulting in a loss of approximately 2% of the sow census as the number of parities increases (Table 2 ). HS2 farms has coefficient "a" close to zero (ranging from − 0.1337 to 0.2916). The quadratic function representing the herd structure is as follows (Fig. 3 ): f(x) = 0.08x 2 − 2.50x + 21.79, with an R 2 value of 0.76 and RMSE = 2.32 (p < 0.001). HS3 is characterised by an upward concave trend curve, with an increase in the percentage of sows in the later parities (an average of 19.0% sows between the 7th to ≥ 8th parity; Table 2 ), compared to the other defined herd structure types. HS3 farms had positive coefficient “a” (ranging from 0.2917 to 1.4762). The quadratic function representing the herd structure is as follows (Fig. 4 ): f(x) = 0.59x 2 – 7.02x + 28.95, with R 2 = 0.57 and RMSE = 4.08 (p < 0.001), indicating a slightly larger average difference between predicted and observed values when compared with the other two types. On the other hand, linear functions were defined for the 3 types of herd structure, fitted to the distribution of sows by parity, depicted in Figs. 2 , 3 , and 4 , along with the quadratic functions. The linear regression models yield R 2 values of 0.66, 0.76, and 0.38, and RMSE values of 3.33, 2.35, and 4.90, respectively, for HS1 farms (f(x) = -2.04x + 21.67), HS2 farms (f(x) = -1.81x + 20.64), and HS3 farms (f(x) = -1.68x + 20.05), indicating a generally poorer fit of these models compared to the previously defined quadratic functions. Comparisons of productive parameters depending on the herd structure The mean productive parameters of farms grouped according to their parity order distribution are shown in Table 3 , showing significant differences between the three types of herd structure. Table 3 Mean (SD) of productive parameters according to the groups of herd structures (N = 623). Herd Structure Type 1 Herd Structure Type 2 Herd Structure Type 3 p-value 1 Effect Size 2 Mean number of sows on the farm 1319.59 (1109.78) 1462.03 (1252.78) 1461.10 (1248.30) 0.451 0.00 Replacement rate 44.50 a (10.45) 47.31 ab (12.36) 50.16 b (17.94) 0.00** 0.02** Number of piglets weaned per sow per year 31.17 a (3.27) 29.53 b (3.29) 28.71 c (3.44) 0.00** 0.07** Culled sow age (months) 31.74 a (10.45) 32.83 ab (10.45) 33.61 b (10.45) 0.00** 0.02** Farrowings per culled sow 4.44 (0.73) 4.58 (0.79) 4.62 (1.22) 0.20 0.01 Total number of piglets weaned per culled sows 55.37 (10.81) 54.43 (10.89) 54.68 (14.91) 0.73 0.00 Farrowings per sow and year 2.44 a (0.06) 2.44 a (0.08) 2.41 b (0.09) 0.00** 0.03** Farrowing rate 87.02 a (4.36) 84.68 b (5.72) 82.11 c (5.96) 0.00** 0.09** Percentage of sows return to oestrus 11.78 a (4.09) 13.85 b (5.64) 16.29 c (5.86) 0.00** 0.08** Weaning-to-first-service interval (WSI) 5.80 a (1.06) 6.15 a (1.61) 6.60 b (2.37) 0.00** 0.03** Weaning-to-oestrus interval (WOI) 4.78 a (0.52) 4.86 a (0.67) 5.11 b (0.95) 0.00** 0.03** Weaning to conception interval (WCI) 8.37 a (2.24) 9.37 b (3.42) 10.38 c (4.04) 0.00** 0.04** Number of piglets total born 16.11 a (1.85) 15.52 b (2.01) 15.35 b (2.10) 0.00** 0.02** Number of piglets born alive 14.79 a (1.56) 14.21 b (1.71) 14.00 b (1.76) 0.00** 0.03** Number of piglets still born 1.33 (0.46) 1.32 (0.48) 1.35 (0.50) 0.76 0.00 Number of piglets weaned 12.75 a (1.33) 12.10 b (1.32) 11.90 b (1.34) 0.00** 0.06** Mortality rate of BA piglets at weaning 13.64 (4.39) 14.65 (4.63) 14.71 (4.55) 0.048* 0.01* Mortality rate of TB piglets at weaning 17.98 (7.09) 18.54 (7.01) 18.59 (6.91) 0.67** 0.00 Abbreviations: a-c Values within a row with different superscripts indicate significant differences between groups. 1 p-value: * p < 0.05; ** p < 0.01 2 Effect size (Cohen's d) classification levels (Cohen, 1988): small (d = 0.01 to < 0.06), medium (d = 0.06 to < 0.14) and large (d ≥ 0.14) effects. HS1 farms, characterised by a slightly concave-downward trend curve, due to a higher percentage of sows in intermediate parities, have the lowest mean age for culled sow and the lowest replacement rate. Both values are not significantly different to those obtained in HS2 farms but are significantly lower than the age for culled sow and replacement rate of HS3 farms. The three herd structure types have significant differences regarding annual productivity (p < 0.01); HS1 farms exhibit the highest mean annual productivity (31.2 PWSY), while HS3 ones, characterised by a higher percentage of sows in the later parities, have the lowest one (28.7 PWSY). Additionally, HS1 farms also have the highest farrowing rate (87.0%), the lowest percentage of sows returning to oestrus (11.8%) and the shortest WCI (8.4 days) (p < 0.01). Similarly, the highest prolificacy (for TB and BA) and number of W piglets were also observed on HS1 farms, with means of 16.1 TB, 14.8 BA and 12.8 W, with significant differences between the groups (p < 0.01). HS1 and HS2 farms have fewer non-productive days, with means of 4.8 and 4.9 days for WOI, and 5.8 and 6.2 days for WSI, respectively (p < 0.01). As a result, these types of farms have a higher number of farrowings per sow per year than HS3 farms (p < 0.01), both with 2.44 farrowings per sow per year. Finally, the effect size of herd structure on these productive parameters has been calculated, revealing that the greatest effects are for farrowing rate, percentage of sows returning to oestrus, annual productivity (PWSY) and W piglets, with a medium effect, with values of η 2 = 0.09, 0.08, 0.07 and 0.06, respectively. The remaining productive parameters showing significant differences among the different types of herd structure have a small effect, with values of η 2 < 0.6. Discussion The present work addresses a study about herd structure and other related reproductive parameters from data gathered in the Spanish Pig Database BDporc®. The mean annual productivity of 29.73 PWSY indicates efficient breeding practices, aligning well with established industry standards in Spain [ 20 ]; therefore, these farms are considered to have high annual productivity [ 21 ]. While these farms demonstrate good productivity outcomes, it is essential to note that these show means of 1.33 SB piglets and pre-weaning mortality rates of 18.4% and 14.4% for piglets TB and BA, respectively, that should be improved. In this regard, these results show piglet survival rates below the minimum target suggested by Sanz-Fernández et al. [ 5 ] which are 83.2% and 88.5% for piglets TB and BA, respectively. Therefore, while these farms exhibit high productivity there is room for improvement. In terms of prolificacy (BA) per parity, it is well known that this varies throughout the reproductive cycles of sows [ 22 ]. In this study, the 3rd and 4th parities prove to be the most productive cycles, in line with the parity curve pattern of litter size reported by Sell-Kubiak et al. [ 23 ]. A drop of almost two piglets was observed between these highly productive cycles and the one with the lowest prolificacy (parities ≥ 8). Besides that, as in the study by Lavery et al. [ 24 ], the WCI decreases as the number of parities increases, with a decline in the number of W piglets from the 3rd parity onwards. Sow longevity, farrowings per culled sow, replacement rate and herd distribution are all inter-related measures. This study reports similar results to those found in a study of 110 commercial breeding herds in Japan by Koketsu [ 25 ], with a mean of 4.55 farrowings at culling and a 47.3% replacement rate. Although currently a replacement rate of 40–50% is considered appropriate for maintaining a proper herd structure [ 26 ], keeping a sow on the farm for a longer time allows for a greater opportunity to recoup the initial investment [ 27 ]. According to Małopolska [ 28 ], the primary reasons for culling sows are reproductive problems, leading to an increase in replacement of sows. This, in turn, results in higher production costs and decreased profitability [ 17 ]. Furthermore, herd longevity is a concern not only from an economic and productive perspective but also from a consumer perspective of animal welfare. Hoge and Bates [ 29 ] suggest improving sow management to extend the productive life of breeding sows to improve both profitability and animal welfare. This is particularly relevant given the increasing societal awareness and concerns about animal welfare and sustainability [ 30 ]. Thus, the use of indicators such as herd longevity may be crucial for evaluating sustainability, animal welfare and management of breeding sows. While several studies have examined models for sow herd management [ 31 ] and sow removal and culling patterns [ 1 , 17 , 32 , 33 ], there remains a gap in understanding the impact of herd structure on farm efficiency. This study bridges that gap by classifying farms into three distinct types or models of herd structure based on the coefficient "a" of quadratic functions fitted to the percentage of sows per parity. These models exhibit distinct shapes and characteristics, significantly contributing to our understanding of sow distribution patterns. Moreover, when analysing the influence of these three types of herd structure on farms' reproductive efficiency within a year, significant differences are observed. HS1 shows a downward concave trend, with a higher percentage of sows in the intermediate parities (3rd to 5th ). This distribution ensures that more than 90% of the sows on the farm are within the 1st to 6th farrowing, allowing them to reach their maximum reproductive potential, as these sows are the most productive [ 4 ]. Thereby, farms HS1 achieve the best productive outcomes, including a higher number of PWSY, surpassing the results of farms with other type of herd structure. Furthermore, Buxadé Carbó et al. [ 34 ] suggested maintaining a higher percentage of sows until the 3rd or 4th parity to maximise their depreciation. This implies that the percentage difference between the 1st and 2nd, and 2nd and 3rd cycles should be minimal, resembling the HS1 defined in this study, which has a higher percentage of sows in intermediate parities. This allows for the maximisation of the number of sows in the most productive parities, achieving higher productivity and reducing the average cost per piglet. HS2 exhibits a trend curve closer to a straight line, maintaining a steady decline in the percentage of sows, aligning with the ideal herd structure defined by Carroll [ 10 ]. This strategy aims to mitigate productivity variations attributed to unstructured herd distribution. On average, this group showed 19.3% of first-parity sows, slightly above the 17% indicated by Carroll [ 10 ] and below the 24.3% reported in the recent cohorts study by Bergman et al. [ 30 ]. Furthermore, the HS2 also aligns with the recommendations of Houška [ 1 ], who suggested that the percentage of sows from the 1st and 2nd parity should be similar to the percentage of sows from the 3rd to the 5th parity; which, in this study, represent 36.3% and 39.6% of the breeding sows census, respectively. Therefore, this census distribution is considered a highly stable herd structure over time. Additionally, farms with HS1 and HS2 also show better productivity in terms of the number of farrowings per sow per year and fewer non-productive days, (i.e., lower values of WSI and WOI) compared to HS3 farms. On the other hand, the census distribution of HS3 farms, with an upward concave trend and a higher percentage of sows in the latest parities (6th to 8th or older) than HS1 and HS2 farms, may be attributed to unstructured herd [ 1 ], with lower productivity results than the other two types of herd structure. This may be due to the higher proportion of older sows, which are less productive [ 4 ]. In addition, this structure had a lower coefficient of determination (R 2 = 0.57) compared to HS1 and HS2 (R 2 = 0.76 in both cases), indicating a slightly larger average difference between predicted and observed values when compared to the other two herd structure types. This difference in the coefficient of determination may be due to a higher variability of farms within this group, including farms with highly unstructured herd distribution. However, this poorer model fit could also be due to the grouping of sows from the 8th farrowing onwards, which could bias the quadratic function, especially in these HS3 farms (≥ 8th parities representing 12% of total sows), not accurately capturing the trend between the last parities. Unfortunately, this information was not available for inclusion in the models, as the BDporc dataset used groups sows from the 8th farrowing onwards. In addition, the linear functions of the three types of herd structure, represented alongside the quadratic functions, exhibit a less precise model fit. Hence, this confirms that a quadratic regression model better fits the reality of the farms' census structure. These results confirm the need to consider herd structure as a relevant factor to evaluate reproductive efficiency. They demonstrate that farms with HS2, traditionally described as ideal or model herd [ 1 , 3 , 10 – 13 , 30 ], do not achieve the best productivity results over the course of a year. Therefore, it is worth questioning whether it should be considered ideal in terms of productivity, even though it maintains a constant herd size between cycles. On the other hand, HS1 achieve the best productivity results and aligns with the herd structure described by De Andrés et al. [ 2 ], who defined an ideal herd structure different from that described by Carroll [ 10 ], with fewer first-parity sows and a higher percentage of sows in the most productive cycles (3rd-4th) by culling fewer sows in these early cycles and maintaining a declining herd size. However, the present study cannot confirm that HS1 is the most productive in the long term, as it has only examined the herd structure and productivity of the farms over a year. In this regard, a proper herd structure must ensure stable productivity over time, with its potential increase as a result of prolificacy, survival rate and fertility improvements. This can potentially be achieved with HS1 and HS2, provided that an appropriate replacement and culling policy is in place. For example, Mote et al. [ 35 ] suggested that producers should aim to limit sow losses to no more than 10% per parity cycle to maintain an ideal herd. However, in some farms, it may be beneficial to increase the percentage of sows in the later parities. In this context, Rodriguez-Zas et al. [ 17 ] recommended that in situations where sow costs are high, salvage or residual values are low, and revenues per piglet are also low, the optimal parity for removal should be between 6 to 10 parities. Additionally, maintaining a herd age structure that retains mature sows allows them to reach their maximum performance [ 30 ], which depends on the management and results of each farm, and would explain why some HS3 farms can achieve good results in terms of productivity. However, it should also be considered that old sows have a higher feed consumption [ 24 ], which increases costs of production and could reduce profitability and sustainability. On the contrary, some farms included in the HS1 group, despite having a higher number of sows in the most productive parities (from 3rd to 5t h ) than HS2 and HS3, have a risk of reducing their productivity in the following year if current young sows (1st and 2nd parities) do not have a low culling rate to maintain the sow census in the future 3th to 5th parities. Therefore, that structure could lead to annual variations in productivity. Considering the above, when organising a farm, it is essential to study its optimal herd structure like any other production parameters and their targets, with the aim of maintaining a consistent replacement and culling policy over time. In any case, this study did not have information on the management techniques implemented on the farms or its health status, nor on the culling rates per parity, which represents a limitation of the study, as it would have provided relevant information to better understand the elimination patterns of different types of herd structure. Conclusions The study provides a comprehensive analysis of sow distribution across parities in commercial sow-breeding farms, providing valuable insights into herd structure and reproductive performance. Although it is difficult to adjust the herd census structure to just three models, this study confirms that the proposed classification of herd structures, based on the coefficient "a" of the quadratic function, allows for the definition of the herd structure based on the curvature of the trend parabola obtained in the regression. Furthermore, this approach of modelling the herd structure also enables the analysis of the association between the distribution of the herd in each parity and farm productive parameters. HS1, with a downward-concave trend, exhibits the best productive outcomes over a year. However, recommending this specific herd distribution as a guarantee for at least medium-term reproductive efficiency would require evaluating how the different defined herd structure types influence the productivity results over consecutive years (e.g., following 1- or 2-year structure and productivity). Therefore, it is necessary to distinguish between the census structure that gives the maximum punctual productivity and the one that most productive farms have or maintain to avoid yearly fluctuations. Therefore, this study does not assess the stability and productivity of farms according to their herd structure over time, which should be the objective of a future research. Finally, these findings highlight the importance of considering herd structure in farm management decisions and suggest that optimising herd structure can contribute to improved reproductive efficiency and productivity on commercial sow farms. Abbreviations BA Piglets born alive HS1 Herd structure type 1 HS2 Herd structure type 2 HS3 Herd structure type 3 PWSY Number of piglets weaned per sow and year SB Stillborn piglets TB Total piglets born W Weaned piglets WCI Weaning to conception interval WOI Weaning-to-oestrus interval WSI Weaning-to-first-service interval Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data supporting the findings of this study are available from the Institute of Agrifood Research and Technology (IRTA), although access is restricted due to licensing agreements governing their use in this study. As a result, these data are not publicly accessible. However, they can be obtained from the authors upon reasonable request and with permission from the Institute of Agrifood Research and Technology (IRTA). Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency, commercial or not-for-profit section. Authors' contributions SSF: Term, Conceptualization, Methodology, Statistic analysis, Validation, Formal analysis, Investigation, Data curation, Writing – original draft. CDG: Methodology, Formal analysis, Investigation, Data curation. JS: Statistic analysis, Conceptualization, Methodology – Review and Editing, Supervision. JCR: Methodology, Statistic analysis, Validation, Formal analysis. NA and LT: Conceptualization, Methodology, Data providers, Supervision. RQ and VRE: Conceptualization, Methodology, Writing – review and editing, Resources, Supervision. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Houška, L. The Relationship between Culling Rate, Herd Structure and Production Efficiency in a Pig Nucleus Herd. Czech J. Anim. Sci. 2009, 54, 365–375, doi:10.17221/1660-CJAS. De Andrés, M.A.; Aparicio, M.; Piñeiro, C. 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Accessed 26 October 2023 Dhuyvetter, K. What Does Attrition Cost and What Is It Worth to Reduce? Proceedings of the Allen D. Leman Swine Conference 27. 2000. Coll. Vet. Med. Univ. Minnesota. Rodriguez-Zas, S.L.; Davis, C.B.; Ellinger, P.N.; Schnitkey, G.D.; Romine, N.M.; Connor, J.F.; Knox, R.V.; Southey, B.R. Impact of Biological and Economic Variables on Optimal Parity for Replacement in Swine Breed-to-Wean Herds1. Journal of Animal Science 2006, 84, 2555–2565, doi:10.2527/jas.2005-635. Ellis, A.B.; Grinstead, P. Hidden Lessons: How a Focus on Slope-like Properties of Quadratic Functions Encouraged Unexpected Generalizations. The Journal of Mathematical Behavior 2008, 27, 277–296, doi:10.1016/j.jmathb.2008.11.002. Kline, R.B. Principles and Practice of Structural Equation Modeling; 5th ed.; Guilford Publications: 370 Seventh Avenue, Suite 1200, New York, NY 10001, 2023; ISBN 978-1-4625-5191-0. Tusell, L.; Alos, N.; Quintanilla, R. La cabaña porcina en cifras: evolución de los principales indicadores bdporc en Capa Blanca e Ibérico. MG Mundo ganadero 2022, 33, 22–25. Koketsu, Y.; Iida, R.; Piñeiro, C. Increased Age at First-Mating Interacting with Herd Size or Herd Productivity Decreases Longevity and Lifetime Reproductive Efficiency of Sows in Breeding Herds. Porc Health Manag 2020, 6, 2, doi:10.1186/s40813-019-0142-9. Koketsu, Y.; Dial, G.D. Factors Influencing the Postweaning Reproductive Performance of Sows on Commercial Farms. Theriogenology 1997, 47, 1445–1461, doi:10.1016/S0093-691X(97)00135-0. Sell-Kubiak, E.; Knol, E.F.; Mulder, H.A. Selecting for Changes in Average “Parity Curve” Pattern of Litter Size in Large White Pigs. Journal of Animal Breeding and Genetics 2019, 136, 134–148, doi:10.1111/jbg.12372. Lavery, A.; Lawlor, P.G.; Magowan, E.; Miller, H.M.; O’Driscoll, K.; Berry, D.P. An Association Analysis of Sow Parity, Live-Weight and Back-Fat Depth as Indicators of Sow Productivity. animal 2019, 13, 622–630, doi:10.1017/S1751731118001799. Koketsu, Y. Longevity and Efficiency Associated with Age Structures of Female Pigs and Herd Management in Commercial Breeding Herds. Journal of Animal Science 2007, 85, 1086–1091, doi:10.2527/jas.2006-493. Vizcaíno, E.; Aparicio, M.; De Andrés, M.A.; Piñeiro, C. How to Reduce the Replacement Rate and Have a Better Parity Distribution Available online: https://www.pig333.com/articles/how-to-reduce-the-replacement-rate-and-have-better-parity-distribution_12458/. Accessed 26 October 2023. Stalder, K.J.; Lacy, R.C.; Cross, T.L.; Conatser, G.E. Financial Impact of Average Parity of Culled Females in a Breed-to-Wean Swine Operation Using Replacement Gilt Net Present Value Analysis. Journal of Swine Health and Production 2003, 11, 69–74. Małopolska, M.M. The Replacement Gilt: Current Strategies for Improvement of the Breeding Herd. JSHAP 2018, 26, 208–214. Hoge, M.D.; Bates, R.O. Developmental Factors That Influence Sow Longevity. J Anim Sci 2011, 89, 1238–1245, doi:10.2527/jas.2010-3175. Bergman, P.; Gröhn, Y.T.; Rajala-Schultz, P.; Virtala, A.-M.; Oliviero, C.; Peltoniemi, O.; Heinonen, M. Sow Removal in Commercial Herds: Patterns and Animal Level Factors in Finland. Prev Vet Med 2018, 159, 30–39, doi:10.1016/j.prevetmed.2018.08.010. Plà, L.M. Review of Mathematical Models for Sow Herd Management. Livestock Science 2007, 106, 107–119, doi:10.1016/j.livsci.2006.09.003. Tani, S.; Piñeiro, C.; Koketsu, Y. Culling in Served Females and Farrowed Sows at Consecutive Parities in Spanish Pig Herds. Porc Health Manag 2018, 4, 3, doi:10.1186/s40813-018-0080-y. Bergman, P.; Munsterhjelm, C.; Virtala, A.-M.; Peltoniemi, O.; Valros, A.; Heinonen, M. Structural Characterization of Piglet Producing Farms and Their Sow Removal Patterns in Finland. Porcine Health Manag 2019, 5, 12, doi:10.1186/s40813-019-0119-8. Buxadé Carbó, C.-I.; Granell, E.M.; Lopez Montes, D. La Cerda Reproductora: Claves de Su Optimizacion Productiva.; Ediciones Euroganadería, 2007. Mote, B.E.; Mabry, J.W.; Stalder, K.J.; Rothschild, M.F. Evaluation of Current Reasons for Removal of Sows from Commercial Farms. The Professional Animal Scientist 2009, 25, 1–7, doi:10.15232/S1080-7446(15)30672-0. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Nov, 2024 Read the published version in Porcine Health Management → Version 1 posted Editorial decision: Revision requested 09 Sep, 2024 Reviews received at journal 09 Sep, 2024 Reviews received at journal 07 Sep, 2024 Reviewers agreed at journal 04 Sep, 2024 Reviewers agreed at journal 02 Sep, 2024 Reviewers invited by journal 08 Jun, 2024 Submission checks completed at journal 03 Jun, 2024 Editor assigned by journal 03 Jun, 2024 First submitted to journal 30 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4504842","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314014503,"identity":"98864d5d-f8f2-44ad-9eeb-32adfb193dc0","order_by":0,"name":"Santos Sanz-Fernández","email":"","orcid":"","institution":"Universidad de Córdoba","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Santos","middleName":"","lastName":"Sanz-Fernández","suffix":""},{"id":314014504,"identity":"14802ce8-ba79-4195-9c70-94e409475b66","order_by":1,"name":"Cipriano Díaz-Gaona","email":"","orcid":"","institution":"Universidad de Córdoba","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cipriano","middleName":"","lastName":"Díaz-Gaona","suffix":""},{"id":314014505,"identity":"e12e0efb-9bb7-4395-ade1-7a7dc7f1df07","order_by":2,"name":"João Simões","email":"","orcid":"","institution":"Universidade de Trás-os-Montes e Alto Douro","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"João","middleName":"","lastName":"Simões","suffix":""},{"id":314014506,"identity":"684e4e97-7a64-482a-b5fb-c7938f856c83","order_by":3,"name":"José Carlos Casas-Rosal","email":"","orcid":"","institution":"Universidad de Córdoba","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"José","middleName":"Carlos","lastName":"Casas-Rosal","suffix":""},{"id":314014507,"identity":"a546bfb2-2d2b-481a-892f-bdb03d620a02","order_by":4,"name":"Nuria Alòs","email":"","orcid":"","institution":"Institute of Agrifood Research and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nuria","middleName":"","lastName":"Alòs","suffix":""},{"id":314014509,"identity":"42e17ad6-a8d9-429c-95b8-60376706c0e4","order_by":5,"name":"Llibertat Tusell","email":"","orcid":"","institution":"Institute of Agrifood Research and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Llibertat","middleName":"","lastName":"Tusell","suffix":""},{"id":314014511,"identity":"c680dac6-da58-4e6e-bc2a-7be4b0568f3a","order_by":6,"name":"Raquel Quintanilla","email":"","orcid":"","institution":"Institute of Agrifood Research and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"Quintanilla","suffix":""},{"id":314014515,"identity":"2546eafb-da87-4d38-abe3-751e013e153a","order_by":7,"name":"Vicente Rodríguez-Estévez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYFAC5gNgip8ELWwJYEqygXgtPAZgyuAAsRrM2Q+YffhQUZe4+UbywwcMFXWEtVj2JCTPnHHmcOK2G2nGBgxnDhPWYnAg4TAzb9sBY7MbOWwSjG1EOM/g/MNmZt5/dcbGM0Ba/hHhMIMbyczMvA3McgYSIC0NzIS1WM54xsw449hhOYkzz4wNEo4R4Rdz/vzPDB9q6nj424EhBmQQ4TAUXgJhDehaRsEoGAWjYBRgAwCKmzaTY9NMTAAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad de Córdoba","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Vicente","middleName":"","lastName":"Rodríguez-Estévez","suffix":""}],"badges":[],"createdAt":"2024-05-30 19:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4504842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4504842/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40813-024-00406-5","type":"published","date":"2024-11-23T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58475153,"identity":"3da7e249-9b7d-4820-a48b-ce4c3e53422a","added_by":"auto","created_at":"2024-06-17 06:46:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91594,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics for (a) the sow distributionand weaning to conception interval per parity; and (b)prolificacy for piglets born alive and weaned per parity (N= 623).\u003c/p\u003e\n\u003cp\u003eBars (I) represent standard error of the mean (SEM).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4504842/v1/8402d50366d5e35998e8f713.png"},{"id":58475154,"identity":"04429a31-c0e2-4805-961b-e5c9eee87c16","added_by":"auto","created_at":"2024-06-17 06:46:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":489868,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuadratic function representation for Herd Structure Type 1 (N=156).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean and median of each cluster of data points at each parity can be found in Table 2.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4504842/v1/e3f19db370907c52c7bb79e3.png"},{"id":58475155,"identity":"7c9cc906-3c37-42ec-b2f6-b6d58be43ace","added_by":"auto","created_at":"2024-06-17 06:46:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuadratic function representation for Herd Structure Type 2 (N=311).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean and median of each cluster of data points at each parity can be found in Table 2.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4504842/v1/0f3cd1d3acb59c7c65098fe8.png"},{"id":58475156,"identity":"1d429afc-cbb5-48c7-93fc-6633ebfc86dd","added_by":"auto","created_at":"2024-06-17 06:46:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":503022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuadratic function representation for Herd Structure Type 3 (N=156).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean and median of each cluster of data points at each parity can be found in Table 2.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4504842/v1/9453c9a6ff6829021bed82ca.png"},{"id":69835739,"identity":"cdfc59be-13ae-4c8e-9fcc-4c337b42b4bd","added_by":"auto","created_at":"2024-11-25 16:14:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2768953,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4504842/v1/1fbe0dbc-a56f-44f1-bcbe-a907915c449c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The impact of herd structure on the performance of commercial sow-breeding farms","fulltext":[{"header":"Background","content":"\u003cp\u003eOne crucial aspect for achieving optimal farm efficiency is to control the herd structure of breeding sows [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], because it directly influences the number of piglets produced within a herd [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The herd structure refers to the distribution of sows within a farm based on the number of reproductive cycles or parities. In general, the herd structure can be divided into three main groups of sows [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]: gilts and primiparous sows, which exhibit lower prolificacy and fewer weaned piglets; mid-parity sows (third to fifth parity), with the highest productivity; and old sows, encompassing sows from the sixth to eighth parity or older, which are those close to being culled and have physiological traits that decrease their productivity. In addition, piglets survival rates also vary across sow reproductive cycles [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] due to variations in colostrum and milk production, immunoglobulin concentration, and birth weight within litters [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, to ensure optimum farm performance, it is crucial to study the distribution of sows across parities (herd structure), as well as to consider other reproductive parameters such as the number of piglets weaned per sow and year (PWSY), the number of farrowings per sow per year, and the sow lifetime performance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, the proportion of sows within each parity group, determined by sow removal and by the maximum number of productive parities at which sows are culled, is a crucial factor in the functionality, productivity and profitability of a pig farm. Carroll [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] defined the ideal herd structure as one that has a gradually decreasing percentage of sows from 1st to 8th parity, which has been widely accepted as the best one for ensuring farm efficiency. This structure suggests a 1st parity sow percentage of \u0026ge;\u0026thinsp;17% and a maximum 8th parity sow percentage of \u0026le;\u0026thinsp;4%. More recently, several authors have also recommended maintaining the percentage of 1st parity sows between 15\u0026ndash;20% breeding sows [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this same line, Koketsu [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] recommended maintaining a stable census, with stable subpopulations of mid-parity sows and mated gilts to optimise herd productivity. Nevertheless, many farms have unstructured sow herd distribution derived from challenges in sow culling or replacement program; which results in worse productivity parameters.\u003c/p\u003e \u003cp\u003eDespite evidence, the impact of herd structure on farm productivity has barely been analysed. The objective of this study is to define different types of herd structures studying 623 Spanish commercial sow farms to assess the relationship between herd structure and farm performance, and to determine which type of herd structure can enhance pig farm reproductive efficiency.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe dataset analysed in the present study comes from the BDporc\u0026reg; databank [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] within the framework of a collaboration agreement between the Institute of Agrifood Research and Technology (IRTA) and the Department of Animal Production of the University of Cordoba. The BDporc\u0026reg; is the main database of sow-breeding farms in Spain, where productive data are collected, through the periodic submission of data generated on the farms, gathered in their own software\u0026rsquo;s for data collection and management. The analysed dataset was collected in 2020 from 623 commercial farms, with a census of 888,479 reproductive sows, representing approximately 40% of the sow-breeding census in Spain. For confidentiality reasons, there is no information available on the genetics of the sows from the studied farms. Nevertheless, these sows are derived from modern commercial lines. Farms with Iberian breed sows were not included. Therefore, the results of this study can be extrapolated beyond Spain, the largest pork producer in Europe and the third globally [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding the farms included in the study, these should have at least records until the 6th parity, in order to evaluate their census structure. A total of 8 farms were excluded from the study for not having data up to 6th parity. Since most of the sows do not reach 8 or more parities and for the shake of simplification, BDporc groups sows with 8 or more parities into a single group. Similarly, these oldest parities are grouped in other studies of intensive farms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] because those sows are close to be culled [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe productive parameters analysed at the farm level have correspond to the data collected over one year, from January to December 2020, and are annual farm averages: number of sows on the farm; replacement rate (proportion of sows newly introduced into the farm relative to the average number of sows present); piglets weaned per sow per year (PWSY); age of sows at culling (months); farrowings per culled sow (average of total farrowings performed by sows until their culling); total number of piglets weaned per culled sow (total number of piglets produced by a sow throughout her life, until its culling); farrowings per sow and year; farrowing rate; percentage of sow return to oestrus; weaning-to-first-service interval (WSI); weaning-to-oestrus interval (WOI); weaning to conception interval (WCI); number of total born (TB), born alive (BA), stillborn (SB) and weaned (W) piglets; mortality rate of TB piglets at weaning; and mortality rate of BA piglets at weaning.\u003c/p\u003e \u003cp\u003eTo analyse the census structure, the percentage of sows within each parity has been calculated, using the total number of litters recorded for each parity over a year (N\u0026thinsp;=\u0026thinsp;1,860,663 litters). Additionally, the average number of BA and W piglets as well as WCI are analysed by parity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eModelling herd structure and classification of farms into groups\u003c/h2\u003e \u003cp\u003eThe farms have been classified into three groups based on the quadratic function of the herd structure of each farm, where the dependent variable is the percentage or proportion of sows, and the independent variable is the parity or cycle of the sow (1st to \u0026ge;\u0026thinsp;8th parity). This function was selected because it is easy-to-interpret and provides a good fit to the real distribution of sow census across parities within farms. Additionally, it enables the assessment of non-linear relationships between the parity number and the sow census, offering a high degree of flexibility to accurately capture complex patterns and variations in the data. To compare the goodness of fit of the different functional forms, the Akaike information criterion has been used. The graphical representation of a quadratic function is a parabola, defined by the equation: f(x)\u0026thinsp;=\u0026thinsp;ax\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;bx\u0026thinsp;+\u0026thinsp;c. The coefficients of the quadratic regression provide information about the shape of the curve and the relationship between variables. Thus, the coefficient \"a\" determines the orientation of the graph, indicating the curvature of the function and whether the parabola opens upwards or downwards [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, the absolute value of \"a\" determines the magnitude of the parabola's curvature and its direction. This \u0026ldquo;a\u0026rdquo; coefficient was calculated by least squares method for quadratic functions using the editor of Python V.3.10.10, Pyzo V.4.12.8.\u003c/p\u003e \u003cp\u003eAccordingly, the three groups of herd structure are based on the value of the coefficient \"a\", classifying the farms according to the 25th, 50th, and 75th percentiles (extreme values and median) of this coefficient; these three groups of herd structures are: type 1 (HS1), corresponding to the percentile 25, with the lowest value of the coefficient \"a\" (negative values; N\u0026thinsp;=\u0026thinsp;156 farms); type 2 (HS2), corresponding to the percentile 50 of the coefficient \"a\" (values closest to zero; N\u0026thinsp;=\u0026thinsp;311 farms); type 3 (HS3), corresponding to the percentile 75, with the highest value of the coefficient \"a\" (positive values; N\u0026thinsp;=\u0026thinsp;156 farms). For each group of herd structure, its quadratic function has been calculated, obtaining the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) and the Root Mean Square Error (RMSE),\u003c/p\u003e \u003cp\u003eAdditionally, the linear function of each herd structure group has been calculated in parallel to compare and validate the fit of the herd structure to a quadratic model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses of the dataset were performed using IBM SPSS\u003csup\u003e\u0026reg;\u003c/sup\u003e 22 software. Descriptive statistics were calculated for the productive parameters of all farms and for each parity, as well as for sow distribution.\u003c/p\u003e \u003cp\u003eTo compare the three types of herd structure, parametric tests were conducted after assessing normality using skewness and kurtosis calculations; to meet normality criteria established by Cohen [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], skewness and kurtosis values should range from \u0026minus;\u0026thinsp;3 to 3 and between \u0026minus;\u0026thinsp;8 and 8, respectively. Specifically, the ANOVA test was performed, followed by the Tukey HSD test to analyse the differences in distribution between the three types of herd structure compared to their productive outcomes. Furthermore, for cases where significant differences were observed, the effect size (η\u003csup\u003e2\u003c/sup\u003e) was calculated to measure the magnitude of the differences found. The effect size of an ANOVA is the value that measures how much the independent variable or factor (the type of herd structure) affects the dependent variable (the productive parameters). Cohen (1988) provides classification benchmarks for effect size levels, defining small effects (η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01 to \u0026lt;\u0026thinsp;0.06), medium effects (η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.06 to \u0026lt;\u0026thinsp;0.14), and large effects (η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.14).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the productive parameters of the farms\u003c/h2\u003e \u003cp\u003eDescriptive statistics of the productive variables associated with the performance of the studied farms are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In general, the census of the studied farms exceeded 1400 breeding sows, with a mean annual productivity of 29.73 PWSY and a mean of 2.43 farrowings per sow per year.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for the productive factors of the commercial farms under study (N\u0026thinsp;=\u0026thinsp;623).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003ePercentiles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean number of sows on the farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1426.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1217.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e561.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e983.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2057.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplacement rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets weaned per sow per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulled sow age (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarrowings per culled sow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of piglets weaned per culled sows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarrowings per sow and year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarrowing rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of sows return to oestrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeaning-to-first-service interval (WSI, days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeaning-to-oestrus interval (WOI, days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeaning to conception interval (WCI, days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets total born (TB, days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets born alive (BA, days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets still born per litter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets weaned per litter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality rate of BA piglets at weaning (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality rate of TB piglets at weaning (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn terms of sow longevity, the average age of culled sows was approximately 33 months, with a mean of 4.55 farrowings and 54.73 piglets weaned per sow lifetime. These farms had a mean replacement rate of 47.31%, with a mean percentage of sows returning to oestrus of nearly 14%, and a mean WCI of 9.37 days.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics for sow distribution, prolificacy and WCI per parity for the total number of farms. Thus, the herd structure of all farms shows a gradual decrease in the percentage of sows from the 1st parity (with a mean of 19.58% sows) to the 7th parity (with a mean of 7.18% sows), approximately maintaining this census in the \u0026ge;\u0026thinsp;8th parities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding litter size, the highest prolificacy for BA piglets was achieved at the 3rd and 4th parities, with means of 14.91 and 14.94 piglets, respectively; while the 2nd parity had the highest number of W piglets (12.51 piglets). On the other hand, the WCI gradually decreased as the number of parities increases, with a mean difference of nearly 6 days between the 2nd and \u0026ge;\u0026thinsp;8th parities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTypes of herd structure\u003c/h2\u003e \u003cp\u003eThe farms were classified into three groups of herd structure (HS1, HS2 and HS3); as stated above this was determined by the coefficient \u0026ldquo;a\u0026rdquo; of the quadratic function fitted to the distribution of sows by parity (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the percentage of sows at each parity (mean and median) and the prolificacy per parity for these three groups of farms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for the sow distribution and prolificacy for piglets born alive and weaned at each parity according to the groups of herd structures (N\u0026thinsp;=\u0026thinsp;623).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParity 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParity 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParity 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eParity 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParity 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eParity 6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eParity 7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eParity\u0026thinsp;\u0026ge;\u0026thinsp;8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHerd Structure Type 1 (a25%)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of sows (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccumulative percentage of sows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e65.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of sows (median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of piglets born alive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of piglets weaned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHerd Structure Type 2 (a50%)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of sows (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccumulative percentage of sows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e85.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of sows (median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of piglets born alive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of piglets weaned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eHerd Structure Type 3 (a75%)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of sows (mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccumulative percentage of sows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e80.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e87.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of sows (median)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of piglets born alive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of piglets weaned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHS1 is characterised by maintaining a higher percentage of sows in the intermediate parities (an average of 45.5% sows between the 3rd to 5th parity; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The obtained quadratic function for this group shows a concave-downward trend curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HS1 farms had negative coefficient \"a\" (ranging from \u0026minus;\u0026thinsp;1.8509 to -0.1346). The quadratic function representing the herd structure is as follows: f(x) = -0.38x\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;1.39x\u0026thinsp;+\u0026thinsp;15.95, with a coefficient of determination R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.76 and RMSE\u0026thinsp;=\u0026thinsp;2.83 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The coefficient of determination R\u003csup\u003e2\u003c/sup\u003e indicates that approximately 76% of the variation in the herd structure within this group of farms can be explained by the quadratic function, and the predicted values by the quadratic function differ from the actual values by approximately 2.83 units on average, indicating a good fit of the model.\u003c/p\u003e \u003cp\u003eHS2 is characterised by a trend curve that is close to a straight line, with a gradual decrease in the percentage of sows from 1st to 8th parity, resulting in a loss of approximately 2% of the sow census as the number of parities increases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). HS2 farms has coefficient \"a\" close to zero (ranging from \u0026minus;\u0026thinsp;0.1337 to 0.2916). The quadratic function representing the herd structure is as follows (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): f(x)\u0026thinsp;=\u0026thinsp;0.08x\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;2.50x\u0026thinsp;+\u0026thinsp;21.79, with an R\u003csup\u003e2\u003c/sup\u003e value of 0.76 and RMSE\u0026thinsp;=\u0026thinsp;2.32 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eHS3 is characterised by an upward concave trend curve, with an increase in the percentage of sows in the later parities (an average of 19.0% sows between the 7th to \u0026ge;\u0026thinsp;8th parity; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), compared to the other defined herd structure types. HS3 farms had positive coefficient \u0026ldquo;a\u0026rdquo; (ranging from 0.2917 to 1.4762). The quadratic function representing the herd structure is as follows (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): f(x)\u0026thinsp;=\u0026thinsp;0.59x\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 7.02x\u0026thinsp;+\u0026thinsp;28.95, with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.57 and RMSE\u0026thinsp;=\u0026thinsp;4.08 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a slightly larger average difference between predicted and observed values when compared with the other two types.\u003c/p\u003e \u003cp\u003eOn the other hand, linear functions were defined for the 3 types of herd structure, fitted to the distribution of sows by parity, depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, along with the quadratic functions. The linear regression models yield R\u003csup\u003e2\u003c/sup\u003e values of 0.66, 0.76, and 0.38, and RMSE values of 3.33, 2.35, and 4.90, respectively, for HS1 farms (f(x) = -2.04x\u0026thinsp;+\u0026thinsp;21.67), HS2 farms (f(x) = -1.81x\u0026thinsp;+\u0026thinsp;20.64), and HS3 farms (f(x) = -1.68x\u0026thinsp;+\u0026thinsp;20.05), indicating a generally poorer fit of these models compared to the previously defined quadratic functions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eComparisons of productive parameters depending on the herd structure\u003c/h2\u003e \u003cp\u003eThe mean productive parameters of farms grouped according to their parity order distribution are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, showing significant differences between the three types of herd structure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean (SD) of productive parameters according to the groups of herd structures (N\u0026thinsp;=\u0026thinsp;623).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHerd Structure Type 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHerd Structure\u003c/p\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHerd Structure\u003c/p\u003e \u003cp\u003eType 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffect Size\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean number of sows on the farm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1319.59 (1109.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1462.03 (1252.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1461.10 (1248.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplacement rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.50\u003csup\u003ea\u003c/sup\u003e (10.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.31\u003csup\u003eab\u003c/sup\u003e (12.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.16\u003csup\u003eb\u003c/sup\u003e (17.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets weaned per sow per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.17\u003csup\u003ea\u003c/sup\u003e (3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.53\u003csup\u003eb\u003c/sup\u003e (3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.71\u003csup\u003ec\u003c/sup\u003e (3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulled sow age (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.74\u003csup\u003ea\u003c/sup\u003e (10.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.83\u003csup\u003eab\u003c/sup\u003e (10.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.61\u003csup\u003eb\u003c/sup\u003e (10.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarrowings per culled sow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.44 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.58 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.62 (1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of piglets weaned per culled sows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.37 (10.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.43 (10.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.68 (14.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarrowings per sow and year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.44\u003csup\u003ea\u003c/sup\u003e (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003csup\u003ea\u003c/sup\u003e (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.41\u003csup\u003eb\u003c/sup\u003e (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarrowing rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.02\u003csup\u003ea\u003c/sup\u003e (4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.68\u003csup\u003eb\u003c/sup\u003e (5.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.11\u003csup\u003ec\u003c/sup\u003e (5.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.09**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of sows return to oestrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.78\u003csup\u003ea\u003c/sup\u003e (4.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.85\u003csup\u003eb\u003c/sup\u003e (5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.29\u003csup\u003ec\u003c/sup\u003e (5.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeaning-to-first-service interval (WSI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.80\u003csup\u003ea\u003c/sup\u003e (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.15\u003csup\u003ea\u003c/sup\u003e (1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.60\u003csup\u003eb\u003c/sup\u003e (2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeaning-to-oestrus interval (WOI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.78\u003csup\u003ea\u003c/sup\u003e (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.86\u003csup\u003ea\u003c/sup\u003e (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.11\u003csup\u003eb\u003c/sup\u003e (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeaning to conception interval (WCI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.37\u003csup\u003ea\u003c/sup\u003e(2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.37\u003csup\u003eb\u003c/sup\u003e (3.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.38\u003csup\u003ec\u003c/sup\u003e (4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets total born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.11\u003csup\u003ea\u003c/sup\u003e (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.52\u003csup\u003eb\u003c/sup\u003e (2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.35\u003csup\u003eb\u003c/sup\u003e (2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets born alive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.79\u003csup\u003ea\u003c/sup\u003e (1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.21\u003csup\u003eb\u003c/sup\u003e (1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.00\u003csup\u003eb\u003c/sup\u003e (1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets still born\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33 (0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of piglets weaned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.75\u003csup\u003ea\u003c/sup\u003e (1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.10\u003csup\u003eb\u003c/sup\u003e (1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.90\u003csup\u003eb\u003c/sup\u003e (1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality rate of BA piglets at weaning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.64 (4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.65 (4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.71 (4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality rate of TB piglets at weaning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.98 (7.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.54 (7.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.59 (6.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: \u003csup\u003ea-c\u003c/sup\u003e Values within a row with different superscripts indicate significant differences between groups. \u003csup\u003e1\u003c/sup\u003ep-value: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e2\u003c/sup\u003eEffect size (Cohen's d) classification levels (Cohen, 1988): small (d\u0026thinsp;=\u0026thinsp;0.01 to \u0026lt;\u0026thinsp;0.06), medium (d\u0026thinsp;=\u0026thinsp;0.06 to \u0026lt;\u0026thinsp;0.14) and large (d\u0026thinsp;\u0026ge;\u0026thinsp;0.14) effects.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHS1 farms, characterised by a slightly concave-downward trend curve, due to a higher percentage of sows in intermediate parities, have the lowest mean age for culled sow and the lowest replacement rate. Both values are not significantly different to those obtained in HS2 farms but are significantly lower than the age for culled sow and replacement rate of HS3 farms. The three herd structure types have significant differences regarding annual productivity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01); HS1 farms exhibit the highest mean annual productivity (31.2 PWSY), while HS3 ones, characterised by a higher percentage of sows in the later parities, have the lowest one (28.7 PWSY). Additionally, HS1 farms also have the highest farrowing rate (87.0%), the lowest percentage of sows returning to oestrus (11.8%) and the shortest WCI (8.4 days) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, the highest prolificacy (for TB and BA) and number of W piglets were also observed on HS1 farms, with means of 16.1 TB, 14.8 BA and 12.8 W, with significant differences between the groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eHS1 and HS2 farms have fewer non-productive days, with means of 4.8 and 4.9 days for WOI, and 5.8 and 6.2 days for WSI, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). As a result, these types of farms have a higher number of farrowings per sow per year than HS3 farms (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), both with 2.44 farrowings per sow per year.\u003c/p\u003e \u003cp\u003eFinally, the effect size of herd structure on these productive parameters has been calculated, revealing that the greatest effects are for farrowing rate, percentage of sows returning to oestrus, annual productivity (PWSY) and W piglets, with a medium effect, with values of η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.09, 0.08, 0.07 and 0.06, respectively. The remaining productive parameters showing significant differences among the different types of herd structure have a small effect, with values of η\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.6.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present work addresses a study about herd structure and other related reproductive parameters from data gathered in the Spanish Pig Database BDporc\u0026reg;.\u003c/p\u003e \u003cp\u003eThe mean annual productivity of 29.73 PWSY indicates efficient breeding practices, aligning well with established industry standards in Spain [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; therefore, these farms are considered to have high annual productivity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. While these farms demonstrate good productivity outcomes, it is essential to note that these show means of 1.33 SB piglets and pre-weaning mortality rates of 18.4% and 14.4% for piglets TB and BA, respectively, that should be improved. In this regard, these results show piglet survival rates below the minimum target suggested by Sanz-Fern\u0026aacute;ndez et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] which are 83.2% and 88.5% for piglets TB and BA, respectively. Therefore, while these farms exhibit high productivity there is room for improvement.\u003c/p\u003e \u003cp\u003eIn terms of prolificacy (BA) per parity, it is well known that this varies throughout the reproductive cycles of sows [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, the 3rd and 4th parities prove to be the most productive cycles, in line with the parity curve pattern of litter size reported by Sell-Kubiak et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A drop of almost two piglets was observed between these highly productive cycles and the one with the lowest prolificacy (parities\u0026thinsp;\u0026ge;\u0026thinsp;8).\u003c/p\u003e \u003cp\u003eBesides that, as in the study by Lavery et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the WCI decreases as the number of parities increases, with a decline in the number of W piglets from the 3rd parity onwards.\u003c/p\u003e \u003cp\u003eSow longevity, farrowings per culled sow, replacement rate and herd distribution are all inter-related measures. This study reports similar results to those found in a study of 110 commercial breeding herds in Japan by Koketsu [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], with a mean of 4.55 farrowings at culling and a 47.3% replacement rate. Although currently a replacement rate of 40\u0026ndash;50% is considered appropriate for maintaining a proper herd structure [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], keeping a sow on the farm for a longer time allows for a greater opportunity to recoup the initial investment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. According to Małopolska [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the primary reasons for culling sows are reproductive problems, leading to an increase in replacement of sows. This, in turn, results in higher production costs and decreased profitability [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, herd longevity is a concern not only from an economic and productive perspective but also from a consumer perspective of animal welfare. Hoge and Bates [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] suggest improving sow management to extend the productive life of breeding sows to improve both profitability and animal welfare. This is particularly relevant given the increasing societal awareness and concerns about animal welfare and sustainability [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Thus, the use of indicators such as herd longevity may be crucial for evaluating sustainability, animal welfare and management of breeding sows.\u003c/p\u003e \u003cp\u003eWhile several studies have examined models for sow herd management [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and sow removal and culling patterns [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], there remains a gap in understanding the impact of herd structure on farm efficiency. This study bridges that gap by classifying farms into three distinct types or models of herd structure based on the coefficient \"a\" of quadratic functions fitted to the percentage of sows per parity. These models exhibit distinct shapes and characteristics, significantly contributing to our understanding of sow distribution patterns. Moreover, when analysing the influence of these three types of herd structure on farms' reproductive efficiency within a year, significant differences are observed.\u003c/p\u003e \u003cp\u003eHS1 shows a downward concave trend, with a higher percentage of sows in the intermediate parities (3rd to 5th ). This distribution ensures that more than 90% of the sows on the farm are within the 1st to 6th farrowing, allowing them to reach their maximum reproductive potential, as these sows are the most productive [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Thereby, farms HS1 achieve the best productive outcomes, including a higher number of PWSY, surpassing the results of farms with other type of herd structure.\u003c/p\u003e \u003cp\u003eFurthermore, Buxad\u0026eacute; Carb\u0026oacute; et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] suggested maintaining a higher percentage of sows until the 3rd or 4th parity to maximise their depreciation. This implies that the percentage difference between the 1st and 2nd, and 2nd and 3rd cycles should be minimal, resembling the HS1 defined in this study, which has a higher percentage of sows in intermediate parities. This allows for the maximisation of the number of sows in the most productive parities, achieving higher productivity and reducing the average cost per piglet.\u003c/p\u003e \u003cp\u003eHS2 exhibits a trend curve closer to a straight line, maintaining a steady decline in the percentage of sows, aligning with the ideal herd structure defined by Carroll [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This strategy aims to mitigate productivity variations attributed to unstructured herd distribution. On average, this group showed 19.3% of first-parity sows, slightly above the 17% indicated by Carroll [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and below the 24.3% reported in the recent cohorts study by Bergman et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, the HS2 also aligns with the recommendations of Houška [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], who suggested that the percentage of sows from the 1st and 2nd parity should be similar to the percentage of sows from the 3rd to the 5th parity; which, in this study, represent 36.3% and 39.6% of the breeding sows census, respectively. Therefore, this census distribution is considered a highly stable herd structure over time. Additionally, farms with HS1 and HS2 also show better productivity in terms of the number of farrowings per sow per year and fewer non-productive days, (i.e., lower values of WSI and WOI) compared to HS3 farms.\u003c/p\u003e \u003cp\u003eOn the other hand, the census distribution of HS3 farms, with an upward concave trend and a higher percentage of sows in the latest parities (6th to 8th or older) than HS1 and HS2 farms, may be attributed to unstructured herd [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with lower productivity results than the other two types of herd structure. This may be due to the higher proportion of older sows, which are less productive [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, this structure had a lower coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.57) compared to HS1 and HS2 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.76 in both cases), indicating a slightly larger average difference between predicted and observed values when compared to the other two herd structure types. This difference in the coefficient of determination may be due to a higher variability of farms within this group, including farms with highly unstructured herd distribution. However, this poorer model fit could also be due to the grouping of sows from the 8th farrowing onwards, which could bias the quadratic function, especially in these HS3 farms (\u0026ge;\u0026thinsp;8th parities representing 12% of total sows), not accurately capturing the trend between the last parities. Unfortunately, this information was not available for inclusion in the models, as the BDporc dataset used groups sows from the 8th farrowing onwards.\u003c/p\u003e \u003cp\u003eIn addition, the linear functions of the three types of herd structure, represented alongside the quadratic functions, exhibit a less precise model fit. Hence, this confirms that a quadratic regression model better fits the reality of the farms' census structure.\u003c/p\u003e \u003cp\u003eThese results confirm the need to consider herd structure as a relevant factor to evaluate reproductive efficiency. They demonstrate that farms with HS2, traditionally described as ideal or model herd [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], do not achieve the best productivity results over the course of a year. Therefore, it is worth questioning whether it should be considered ideal in terms of productivity, even though it maintains a constant herd size between cycles. On the other hand, HS1 achieve the best productivity results and aligns with the herd structure described by De Andr\u0026eacute;s et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], who defined an ideal herd structure different from that described by Carroll [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], with fewer first-parity sows and a higher percentage of sows in the most productive cycles (3rd-4th) by culling fewer sows in these early cycles and maintaining a declining herd size. However, the present study cannot confirm that HS1 is the most productive in the long term, as it has only examined the herd structure and productivity of the farms over a year.\u003c/p\u003e \u003cp\u003eIn this regard, a proper herd structure must ensure stable productivity over time, with its potential increase as a result of prolificacy, survival rate and fertility improvements. This can potentially be achieved with HS1 and HS2, provided that an appropriate replacement and culling policy is in place. For example, Mote et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] suggested that producers should aim to limit sow losses to no more than 10% per parity cycle to maintain an ideal herd. However, in some farms, it may be beneficial to increase the percentage of sows in the later parities. In this context, Rodriguez-Zas et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] recommended that in situations where sow costs are high, salvage or residual values are low, and revenues per piglet are also low, the optimal parity for removal should be between 6 to 10 parities. Additionally, maintaining a herd age structure that retains mature sows allows them to reach their maximum performance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which depends on the management and results of each farm, and would explain why some HS3 farms can achieve good results in terms of productivity. However, it should also be considered that old sows have a higher feed consumption [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which increases costs of production and could reduce profitability and sustainability.\u003c/p\u003e \u003cp\u003eOn the contrary, some farms included in the HS1 group, despite having a higher number of sows in the most productive parities (from 3rd to 5t\u003csup\u003eh\u003c/sup\u003e) than HS2 and HS3, have a risk of reducing their productivity in the following year if current young sows (1st and 2nd parities) do not have a low culling rate to maintain the sow census in the future 3th to 5th parities. Therefore, that structure could lead to annual variations in productivity. Considering the above, when organising a farm, it is essential to study its optimal herd structure like any other production parameters and their targets, with the aim of maintaining a consistent replacement and culling policy over time. In any case, this study did not have information on the management techniques implemented on the farms or its health status, nor on the culling rates per parity, which represents a limitation of the study, as it would have provided relevant information to better understand the elimination patterns of different types of herd structure.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe study provides a comprehensive analysis of sow distribution across parities in commercial sow-breeding farms, providing valuable insights into herd structure and reproductive performance. Although it is difficult to adjust the herd census structure to just three models, this study confirms that the proposed classification of herd structures, based on the coefficient \"a\" of the quadratic function, allows for the definition of the herd structure based on the curvature of the trend parabola obtained in the regression. Furthermore, this approach of modelling the herd structure also enables the analysis of the association between the distribution of the herd in each parity and farm productive parameters.\u003c/p\u003e \u003cp\u003eHS1, with a downward-concave trend, exhibits the best productive outcomes over a year. However, recommending this specific herd distribution as a guarantee for at least medium-term reproductive efficiency would require evaluating how the different defined herd structure types influence the productivity results over consecutive years (e.g., following 1- or 2-year structure and productivity). Therefore, it is necessary to distinguish between the census structure that gives the maximum punctual productivity and the one that most productive farms have or maintain to avoid yearly fluctuations.\u003c/p\u003e \u003cp\u003e Therefore, this study does not assess the stability and productivity of farms according to their herd structure over time, which should be the objective of a future research.\u003c/p\u003e \u003cp\u003eFinally, these findings highlight the importance of considering herd structure in farm management decisions and suggest that optimising herd structure can contribute to improved reproductive efficiency and productivity on commercial sow farms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Piglets born alive\u003c/p\u003e\n\u003cp\u003eHS1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Herd structure type 1\u003c/p\u003e\n\u003cp\u003eHS2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Herd structure type 2\u003c/p\u003e\n\u003cp\u003eHS3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Herd structure type 3\u003c/p\u003e\n\u003cp\u003ePWSY\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Number of piglets weaned per sow and year\u003c/p\u003e\n\u003cp\u003eSB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Stillborn piglets\u003c/p\u003e\n\u003cp\u003eTB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total piglets born\u003c/p\u003e\n\u003cp\u003eW\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Weaned piglets\u003c/p\u003e\n\u003cp\u003eWCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Weaning to conception interval\u003c/p\u003e\n\u003cp\u003eWOI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Weaning-to-oestrus interval\u003c/p\u003e\n\u003cp\u003eWSI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Weaning-to-first-service interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the Institute of Agrifood Research and Technology (IRTA), although access is restricted due to licensing agreements governing their use in this study. As a result, these data are not publicly accessible. However, they can be obtained from the authors upon reasonable request and with permission from the Institute of Agrifood Research and Technology (IRTA).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency, commercial or not-for-profit section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSSF: Term, Conceptualization, Methodology, Statistic analysis, Validation, Formal analysis, Investigation, Data curation, Writing \u0026ndash; original draft. CDG: Methodology, Formal analysis, Investigation, Data curation. JS: Statistic analysis, Conceptualization, Methodology \u0026ndash; Review and Editing, Supervision. JCR: Methodology, Statistic analysis, Validation, Formal analysis. NA and LT: Conceptualization, Methodology, Data providers, Supervision. RQ and VRE: Conceptualization, Methodology, Writing \u0026ndash; review and editing, Resources, Supervision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHou\u0026scaron;ka, L. The Relationship between Culling Rate, Herd Structure and Production Efficiency in a Pig Nucleus Herd. Czech J. Anim. Sci. 2009, 54, 365\u0026ndash;375, doi:10.17221/1660-CJAS.\u003c/li\u003e\n\u003cli\u003eDe Andr\u0026eacute;s, M.A.; Aparicio, M.; Pi\u0026ntilde;eiro, C. La estructura censal ideal ya no es un tri\u0026aacute;ngulo Available online: https://www.3tres3.com/latam/articulos/la-estructura-censal-ideal-ya-no-es-un-triangulo_11962/. Accessed 19 June 2023.\u003c/li\u003e\n\u003cli\u003eLawlor, P.G.; Lynch, P.B. A Review of Factors Influencing Litter Size in Irish Sows. Irish Veterinary Journal 2007, 60, 359, doi:10.1186/2046-0481-60-6-359.\u003c/li\u003e\n\u003cli\u003eKoketsu, Y. Within-Farm Variability in Age Structure of Breeding-Female Pigs and Reproductive Performance on Commercial Swine Breeding Farms. Theriogenology 2005, 63, 1256\u0026ndash;1265, doi:10.1016/j.theriogenology.2004.04.018.\u003c/li\u003e\n\u003cli\u003eSanz-Fern\u0026aacute;ndez, S.; D\u0026iacute;az-Gaona, C.; Casas-Rosal, J.C.; Al\u0026ograve;s, N.; Tusell, L.; Quintanilla, R.; Rodr\u0026iacute;guez-Est\u0026eacute;vez, V. Preweaning Piglet Survival on Commercial Farms. Journal of Animal Science 2024, 102, skad408, doi:10.1093/jas/skad408.\u003c/li\u003e\n\u003cli\u003eCarney-Hinkle, E.E.; Tran, H.; Bundy, J.W.; Moreno, R.; Miller, P.S.; Burkey, T.E. Effect of Dam Parity on Litter Performance, Transfer of Passive Immunity, and Progeny Microbial Ecology1. Journal of Animal Science 2013, 91, 2885\u0026ndash;2893, doi:10.2527/jas.2011-4874.\u003c/li\u003e\n\u003cli\u003eWegner, K.; Lambertz, C.; Das, G.; Reiner, G.; Gauly, M. Effects of Temperature and Temperature-Humidity Index on the Reproductive Performance of Sows during Summer Months under a Temperate Climate. Animal Science Journal 2016, 87, 1334\u0026ndash;1339, doi:10.1111/asj.12569.\u003c/li\u003e\n\u003cli\u003eAmatucci, L.; Luise, D.; Correa, F.; Bosi, P.; Trevisi, P. Importance of Breed, Parity and Sow Colostrum Components on Litter Performance and Health. Animals 2022, 12, 1230, doi:10.3390/ani12101230.\u003c/li\u003e\n\u003cli\u003eKoketsu, Y.; Tani, S.; Iida, R. Factors for Improving Reproductive Performance of Sows and Herd Productivity in Commercial Breeding Herds. Porcine Health Management 2017, 3, 1, doi:10.1186/s40813-016-0049-7.\u003c/li\u003e\n\u003cli\u003eCarroll, C. Sow Culling and Parity Profiles. Proceedings of Teagasc Pig Farmers Conferences. 1999, Teagasc, Sandymount Avenue, Dublin 4, 35-41.\u003c/li\u003e\n\u003cli\u003eCasanovas, C. Estructura del censo (I) Available online: https://www.3tres3.com/articulos/estructura-del-censo-i_4261/. Accessed 29 September 2020.\u003c/li\u003e\n\u003cli\u003eSoede, N.M.; Hoving, L.L.; Leeuwen, J.J.J. van; Kemp, B. The Second Litter Syndrome in Sows; Causes, Consequences and Possibilities of Prevention.; 2013; pp. 28\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eOrdaz-Ochoa, G.; Ju\u0026aacute;rez-Caratachea, A.; Garc\u0026iacute;a-Valladares, A.; P\u0026eacute;rez-S\u0026aacute;nchez, R.E. EVALUACI\u0026Oacute;N PRODUCTIVA Y AN\u0026Aacute;LISIS COSTO-BENEFICIO DEL ESQUEMA DE PRODUCCI\u0026Oacute;N PORCINA: PRIMER PARTO-ELIMINACI\u0026Oacute;N DE CERDAS. Revista Cient\u0026iacute;fica 2014.\u003c/li\u003e\n\u003cli\u003eIRTA bdporc. Available online: https://bdporc.irta.es/. Accessed 29 May 2024.\u003c/li\u003e\n\u003cli\u003eMAPA, M. de A., Pesca y Alimentaci\u0026oacute;n \u0026ldquo;EL SECTOR DE LA CARNE DE CERDO EN CIFRAS: Principales Indicadores Econ\u0026oacute;micos\u0026rdquo; Subdirecci\u0026oacute;n General de Producciones Ganaderas y Cineg\u0026eacute;ticas, Direcci\u0026oacute;n General de Producciones y Mercados Agrarios.; 2023; Available online: https://www.mapa.gob.es/es/ganaderia/temas/produccion-y-mercados-ganaderos/indicadoreseconomicossectorporcino2022_tcm30-564427.pdf. Accessed 26 October 2023\u003c/li\u003e\n\u003cli\u003eDhuyvetter, K. What Does Attrition Cost and What Is It Worth to Reduce? Proceedings of the Allen D. Leman Swine Conference 27. 2000. Coll. Vet. Med. Univ. Minnesota.\u003c/li\u003e\n\u003cli\u003eRodriguez-Zas, S.L.; Davis, C.B.; Ellinger, P.N.; Schnitkey, G.D.; Romine, N.M.; Connor, J.F.; Knox, R.V.; Southey, B.R. Impact of Biological and Economic Variables on Optimal Parity for Replacement in Swine Breed-to-Wean Herds1. Journal of Animal Science 2006, 84, 2555\u0026ndash;2565, doi:10.2527/jas.2005-635.\u003c/li\u003e\n\u003cli\u003eEllis, A.B.; Grinstead, P. Hidden Lessons: How a Focus on Slope-like Properties of Quadratic Functions Encouraged Unexpected Generalizations. The Journal of Mathematical Behavior 2008, 27, 277\u0026ndash;296, doi:10.1016/j.jmathb.2008.11.002.\u003c/li\u003e\n\u003cli\u003eKline, R.B. Principles and Practice of Structural Equation Modeling; 5th ed.; Guilford Publications: 370 Seventh Avenue, Suite 1200, New York, NY 10001, 2023; ISBN 978-1-4625-5191-0.\u003c/li\u003e\n\u003cli\u003eTusell, L.; Alos, N.; Quintanilla, R. La caba\u0026ntilde;a porcina en cifras: evoluci\u0026oacute;n de los principales indicadores bdporc en Capa Blanca e Ib\u0026eacute;rico. MG Mundo ganadero 2022, 33, 22\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eKoketsu, Y.; Iida, R.; Pi\u0026ntilde;eiro, C. Increased Age at First-Mating Interacting with Herd Size or Herd Productivity Decreases Longevity and Lifetime Reproductive Efficiency of Sows in Breeding Herds. Porc Health Manag 2020, 6, 2, doi:10.1186/s40813-019-0142-9.\u003c/li\u003e\n\u003cli\u003eKoketsu, Y.; Dial, G.D. Factors Influencing the Postweaning Reproductive Performance of Sows on Commercial Farms. Theriogenology 1997, 47, 1445\u0026ndash;1461, doi:10.1016/S0093-691X(97)00135-0.\u003c/li\u003e\n\u003cli\u003eSell-Kubiak, E.; Knol, E.F.; Mulder, H.A. Selecting for Changes in Average \u0026ldquo;Parity Curve\u0026rdquo; Pattern of Litter Size in Large White Pigs. Journal of Animal Breeding and Genetics 2019, 136, 134\u0026ndash;148, doi:10.1111/jbg.12372.\u003c/li\u003e\n\u003cli\u003eLavery, A.; Lawlor, P.G.; Magowan, E.; Miller, H.M.; O\u0026rsquo;Driscoll, K.; Berry, D.P. An Association Analysis of Sow Parity, Live-Weight and Back-Fat Depth as Indicators of Sow Productivity. animal 2019, 13, 622\u0026ndash;630, doi:10.1017/S1751731118001799.\u003c/li\u003e\n\u003cli\u003eKoketsu, Y. Longevity and Efficiency Associated with Age Structures of Female Pigs and Herd Management in Commercial Breeding Herds. Journal of Animal Science 2007, 85, 1086\u0026ndash;1091, doi:10.2527/jas.2006-493.\u003c/li\u003e\n\u003cli\u003eVizca\u0026iacute;no, E.; Aparicio, M.; De Andr\u0026eacute;s, M.A.; Pi\u0026ntilde;eiro, C. How to Reduce the Replacement Rate and Have a Better Parity Distribution Available online: https://www.pig333.com/articles/how-to-reduce-the-replacement-rate-and-have-better-parity-distribution_12458/. Accessed 26 October 2023.\u003c/li\u003e\n\u003cli\u003eStalder, K.J.; Lacy, R.C.; Cross, T.L.; Conatser, G.E. Financial Impact of Average Parity of Culled Females in a Breed-to-Wean Swine Operation Using Replacement Gilt Net Present Value Analysis. Journal of Swine Health and Production 2003, 11, 69\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eMałopolska, M.M. The Replacement Gilt: Current Strategies for Improvement of the Breeding Herd. JSHAP 2018, 26, 208\u0026ndash;214.\u003c/li\u003e\n\u003cli\u003eHoge, M.D.; Bates, R.O. Developmental Factors That Influence Sow Longevity. J Anim Sci 2011, 89, 1238\u0026ndash;1245, doi:10.2527/jas.2010-3175.\u003c/li\u003e\n\u003cli\u003eBergman, P.; Gr\u0026ouml;hn, Y.T.; Rajala-Schultz, P.; Virtala, A.-M.; Oliviero, C.; Peltoniemi, O.; Heinonen, M. Sow Removal in Commercial Herds: Patterns and Animal Level Factors in Finland. Prev Vet Med 2018, 159, 30\u0026ndash;39, doi:10.1016/j.prevetmed.2018.08.010.\u003c/li\u003e\n\u003cli\u003ePl\u0026agrave;, L.M. Review of Mathematical Models for Sow Herd Management. Livestock Science 2007, 106, 107\u0026ndash;119, doi:10.1016/j.livsci.2006.09.003.\u003c/li\u003e\n\u003cli\u003eTani, S.; Pi\u0026ntilde;eiro, C.; Koketsu, Y. Culling in Served Females and Farrowed Sows at Consecutive Parities in Spanish Pig Herds. Porc Health Manag 2018, 4, 3, doi:10.1186/s40813-018-0080-y.\u003c/li\u003e\n\u003cli\u003eBergman, P.; Munsterhjelm, C.; Virtala, A.-M.; Peltoniemi, O.; Valros, A.; Heinonen, M. Structural Characterization of Piglet Producing Farms and Their Sow Removal Patterns in Finland. Porcine Health Manag 2019, 5, 12, doi:10.1186/s40813-019-0119-8.\u003c/li\u003e\n\u003cli\u003eBuxad\u0026eacute; Carb\u0026oacute;, C.-I.; Granell, E.M.; Lopez Montes, D. La Cerda Reproductora: Claves de Su Optimizacion Productiva.; Ediciones Euroganader\u0026iacute;a, 2007.\u003c/li\u003e\n\u003cli\u003eMote, B.E.; Mabry, J.W.; Stalder, K.J.; Rothschild, M.F. Evaluation of Current Reasons for Removal of Sows from Commercial Farms. The Professional Animal Scientist 2009, 25, 1\u0026ndash;7, doi:10.15232/S1080-7446(15)30672-0.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"porcine-health-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"phmj","sideBox":"Learn more about [Porcine Health Management](http://porcinehealthmanagement.biomedcentral.com/)","snPcode":"40813","submissionUrl":"https://submission.nature.com/new-submission/40813/3","title":"Porcine Health Management","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"replacement rate, census structure, parity, breeding sows, reproductive performance","lastPublishedDoi":"10.21203/rs.3.rs-4504842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4504842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe herd structure, i.e., distribution of sows within a farm based on their parity number, and its management are essential to optimise farm reproductive efficiency. The objective of this study is to define different types of herd structure using data from 623 Spanish commercial sow farms. Additionally, this study aims to determine which type of herd structure can enhance reproductive efficiency at the farm level.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFarms are classified into three groups according to the quadratic function fitted to the percentage of sows over parities. This classification unveils three types of herd structures: type 1 (HS1) exhibits a concave-downward trend, with a higher percentage of sows in intermediate parities (mean of 45.5% sows between the 3rd to 5th parity); type 2 (HS2) presents a trend curve that is close to a straight line, with a gradual decrease in the percentage of sows per parity (approximately 2% loss of sows census per parity); and type 3 (HS3) shows an upward concave trend curve, with an increase in the percentage of sows in later parities (19.0% of sows between 7th and \u0026ge;\u0026thinsp;8th parity). Additionally, parametric tests (ANOVA followed by the Tukey HSD test) assess productivity differences between the three groups of farms with different herd structures. Significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) are noted in number of piglets weaned per sow per year, farrowing rate, percentage of sows returning to oestrus and number of weaned piglets, with a medium effect size (values of η\u003csup\u003e2\u003c/sup\u003e between 0.06 to \u0026lt;\u0026thinsp;0.14). Farms with HS1 (showing a concave-downward trend) have the best productive outcomes over a year, surpassing the results of farms with HS2 and even more so those of HS3 farms.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study shows the importance of herd structure on sow-breeding farms as factor of reproductive efficiency. The results endorse the proposed classification based on the curvature of the trend parabola obtained with the quadratic function to categorize herd structures into three groups. Besides that, these highlight the importance of considering the herd structure in farm decision-making.\u003c/p\u003e","manuscriptTitle":"The impact of herd structure on the performance of commercial sow-breeding farms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-17 06:46:32","doi":"10.21203/rs.3.rs-4504842/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-09T18:46:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T14:32:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-07T19:48:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234637579608166060542104710118884451233","date":"2024-09-04T06:57:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5053868928167776586770666224853192140","date":"2024-09-02T14:47:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-08T11:35:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-03T08:04:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-03T08:04:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Porcine Health Management","date":"2024-05-30T19:21:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"porcine-health-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"phmj","sideBox":"Learn more about [Porcine Health Management](http://porcinehealthmanagement.biomedcentral.com/)","snPcode":"40813","submissionUrl":"https://submission.nature.com/new-submission/40813/3","title":"Porcine Health Management","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"652c7080-aa83-4616-89af-f067f3597674","owner":[],"postedDate":"June 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:09:45+00:00","versionOfRecord":{"articleIdentity":"rs-4504842","link":"https://doi.org/10.1186/s40813-024-00406-5","journal":{"identity":"porcine-health-management","isVorOnly":false,"title":"Porcine Health Management"},"publishedOn":"2024-11-23 15:57:25","publishedOnDateReadable":"November 23rd, 2024"},"versionCreatedAt":"2024-06-17 06:46:32","video":"","vorDoi":"10.1186/s40813-024-00406-5","vorDoiUrl":"https://doi.org/10.1186/s40813-024-00406-5","workflowStages":[]},"version":"v1","identity":"rs-4504842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4504842","identity":"rs-4504842","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}