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Financial impact of average parity of culled females in a breed-to-wean swine operation using replacement gilt net present value analysis

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of culled females in a breed-to-wean swine operation using replacement gilt net present value analysis. J Swine Health Prod. 2003;11(2):69-74. Summary A net present value (NPV) analysis was used to determine the number of parities a sow must remain in the breeding herd of a breed-to-wean operation before the initial investment in her is profitable, and to evaluate the sensitivity of NPV to produc-tion, price received, and gilt replacement price. On the basis of 1996–2000 average production levels, segregated early weaning (SEW) pig price, production costs, and equity information, a replacement gilt must remain in the breeding herd for three parities before reaching a positive NPV. Sows having a longer productive herd life, when SEW price is at profitable levels, pro-duce a higher NPV, and thus are more profitable investments than females which remain in the herd for shorter periods of time.
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... Giving birth is called a "parity;" sows that have not yet given birth are called "replacement gilt;" and researchers calculate how much value can be added by taking piglets away from their nursing mothers as "segregated early weaning (SEW) pig price." Relevant articles have titles like this: "Financial Impact of Average Parity of Culled Females in a Breed-to-Wean Swine Operation using Replacement Gilt Net Present Value Analysis" (Stalder et al., 2003). Factoring in demand, per capita consumption of each genre of animal product (e.g., packaged cuts, hot dogs, canned meats) is projected ten years ahead to help guide investments. ...
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Slaughtering and processing animals on an industrial scale are complicated activities that are both highly prominent in the food system and intentionally concealed from the public. The structural violence enacted within this industry deserves attention as a direct form of state-corporate harm. To this end, this paper invokes Tombs and Whyte’s imaginaries of corporate crime to understand what they might call the “ceaseless repetition” through which harms against workers, animals, and the environment in the meatpacking industry are enacted. From this application in the context of a Marxian analysis of primitive accumulation, we have determined the importance of capital’s ability to capture, commodify, and kill (CCK) – concepts we offer together as a heuristic device to highlight the types of harm done to labor, animals, and land, and to explain how these harms are accomplished. Drawing on the copious literature on meatpacking, as well as our own direct observations of the industry, this paper emphasizes the role of food systems for white-collar and corporate crime specialists interested in policy and social change.
... 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. ...
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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 η² 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.
... The high removal of young parity sows destabilizes the herd structure and is associated with increased purchase of replacement gilts. A sow needs to stay in the herd for a minimum of three parities in order for the producer to realize its investment value [25][26][27], implying that such sows have the chance to recoup their initial replacement costs. ...
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This study aimed to characterize and quantify reasons for the removal of nurse sows and identify the removal associated with their extended lactation length (ELL). A total of 100,756 removed nurse sows within a period of 2016–2022 from 53 sow herds in the Midwest USA were analyzed. Reproductive failure was the most common removal reason (χ2 = 8748.421, p < 0.001) affecting P1, P2, and P3 nurse sows. Failure to conceive and absence of estrus were the main causes of reproductive failure (χ2 = 352.480, p < 0.001) affecting P1 and P2 nurse sows and P1 and P5 nurse sows, respectively. When P2 and P6 nurse sows had an ELL of 0–7 d, they faced a high chance (χ2 = 13.312, p = 0.021) of removal due to conception failure and failure to return to heat, respectively. When P2 and P5 nurse sows had an ELL of 8–14 d, they were highly vulnerable (χ2 = 59.847, p < 0.001) to removal due to failure to conceive and showing heat, respectively. Finally, when ELL was at 15–21 days, P4 and P5 nurse sows were more likely (χ2 = 41.751, p < 0.001) to be removed due to failure to express heat, whereas at the same time, P2 and P3 nurse sows experienced the same removal threat due to failing to conceive. These results could help producers manage nurse sow systems.
... Moreover, human observation cannot satisfy the requirements of real-time monitoring for 24 h [54]. To avoid economic loss on the investment in a single sow, she is required to produce at least three litters before culling [55]. However, many sows are replaced before they reach the break-even point [56]. ...
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Simple Summary Accurate estrus detection by farmers is key to successful sow breeding as it offers the opportunity for timely mating, successful reproduction, and increased productivity. Traditional estrus detection methods based on physiological changes, mounting, and back pressure tests often fall short in accuracy, leading to lower conception rates and smaller litters. To address these issues, researchers are exploring modern technologies to study parameters such as vulvar temperature, posture, movement, and sound in relation to estrus. This review examines the effectiveness of these modern estrus detection techniques, and the findings indicate that they can enhance the accuracy of heat detection compared to conventional methods. Abstract In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.
... Selection of replacement females is a critical decision for commercial producers, sometimes based on little-known information outside of pedigrees and physical conformation [1]. From an economic standpoint, maintaining a productive, healthy sow herd while placing emphasis on female longevity is of high importance [2][3][4]. However, sow longevity and productivity can be ambiguously defined terms across different production systems. ...
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