Evidence map›Paper›PMID 40537870›Full record

ArticlePorcine health management2025

Temporal evolution of Sow herd age structure and its impact on the performance of Spanish commercial farms.

S Sanz-Fernández, C Díaz-Gaona, J Simões, J C Casas-Rosal, N Alòs, L Tusell, R Quintanilla, Vicente Rodríguez-Estévez

Abstract read
In one paragraph

Article in Porcine health management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

S Sanz-FernándezDepartamento de Producción Animal, IC Zoonosis y Enfermedades Emergentes ENZOEM, Facultad de Veterinaria, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Campus de Rabanales, Córdoba, 14071, España.
C Díaz-GaonaDepartamento de Producción Animal, IC Zoonosis y Enfermedades Emergentes ENZOEM, Facultad de Veterinaria, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Campus de Rabanales, Córdoba, 14071, España.
J SimõesDepartment of Veterinary Science, CECAV-Animal and Veterinary Research Center, AL4AnimalS-Associate Laboratory for Animal and Veterinary Sciences, University of Trás-os-Montes e Alto Douro, Vila Real, 5000-801, Portugal.
J C Casas-RosalDepartamento de Matemáticas, Universidad de Córdoba, Avd. San Alberto Magno s/n, Córdoba, 14071, España.
N AlòsAnimal Breeding and Genetics Program, IRTA, Torre Marimon, Caldes de Montbui, 08140, Spain.
L TusellAnimal Breeding and Genetics Program, IRTA, Torre Marimon, Caldes de Montbui, 08140, Spain.
R QuintanillaAnimal Breeding and Genetics Program, IRTA, Torre Marimon, Caldes de Montbui, 08140, Spain.
Vicente Rodríguez-EstévezDepartamento de Producción Animal, IC Zoonosis y Enfermedades Emergentes ENZOEM, Facultad de Veterinaria, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Campus de Rabanales, Córdoba, 14071, España. pa2roesv@uco.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEfficient herd management is crucial for maintaining constant pig production. This study analysed the evolution of census structure over time and how productive performance variables related to farm efficiency are affected and evolved, using data from 427 Spanish commercial pig farms over three years (2020-2022). Farms were classified into three types of herd age structures based on the first coefficient of a quadratic function representing sow parity distribution. A longitudinal analysis was performed to evaluate changes in herd age structure and productivity over time, applying repeated measures ANOVA.

resultsThe herd age structure types in 2020 were: HS1 (with a downward-concave trend), HS2 (with a trend close to a straight line), and HS3 (with an upward-concave trend). HS1 farms had the highest productivity over time, maintaining superior performance compared to HS2 and HS3 (p < 0.01), with 31.4 piglets weaned per sow per year in 2021 and 30.9 in 2022. However, HS1 farms showed moderate consistency in herd parity structure over time, with 48.6% remaining in the same group in 2021 and 43% in 2022. HS2 farms showed the greatest herd parity structure stability over time, with 60.1% and 54.5% of farms remaining in this group in 2021 and 2022, respectively. HS3 farms were the least stable, with only 29% retaining their classification by 2022, and had the lowest productivity.

conclusionsClassifying farms by herd age structure provides valuable insights into how parity distribution influences farm productivity over time and how herd parity structure evolves. HS1 farms achieved the best productivity over the study but require specific management practices to maintain their stability. HS2 remained with the most stable herd structure over time, with intermediate productivity, while HS3 farms showed the worst stability and performance. Thus, HS1 is recommended as the optimal herd age structure for maximizing productivity in the short and medium term. Further research should focus on identifying specific management factors to optimize productivity and ensure long-term herd structure stability of HS1.

Indexed as

Census stabilityCulling rateFarm productivityHerd managementParity distribution

Identifiers

PMID40537870
PMCPMC12180210

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.