Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient
Management of hyperprolific sows is challenging when it comes to controlling birth weight variability and piglet survival in large litters. The growth of low birth weight piglets can be compromised and have a negative impact on production efficiency. The objective of the study was to apply a random...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/13/18/2888 |
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| author | Dubravko Škorput Nina Jančo Danijel Karolyi Ana Kaić Zoran Luković |
| author_facet | Dubravko Škorput Nina Jančo Danijel Karolyi Ana Kaić Zoran Luković |
| author_sort | Dubravko Škorput |
| collection | DOAJ |
| description | Management of hyperprolific sows is challenging when it comes to controlling birth weight variability and piglet survival in large litters. The growth of low birth weight piglets can be compromised and have a negative impact on production efficiency. The objective of the study was to apply a random regression coefficient model to estimate the main effects of the growth of piglets of highly prolific sows. The dataset contained growth data for 360 piglets from 25 Pen Ar Lan Naima sows. In addition to routine procedures after farrowing, piglets were weighed five times: on day 1 after farrowing, on day 14 of life, at weaning on day 28, on day 30 of nursery period, and at the end of the nursery period when piglets were 83 days old. Data were treated as longitudinal, with body weight as the dependent variable. Fitting age as a quadratic regression within piglets in the random part of the model helped to determine the significant effect of birth weight, litter size, and parity on the growth of the piglets. Since the piglets from large litters often have non-uniform birth weights and this can affect further growth, the use of a random regression coefficient model is practical for analysing the growth of such piglets due to the ability to describe the individual growth pattern of every individual. |
| first_indexed | 2024-03-10T23:07:18Z |
| format | Article |
| id | doaj.art-463bbfab278f4d15b71d3f83762d5e88 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-10T23:07:18Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-463bbfab278f4d15b71d3f83762d5e882023-11-19T09:15:00ZengMDPI AGAnimals2076-26152023-09-011318288810.3390/ani13182888Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression CoefficientDubravko Škorput0Nina Jančo1Danijel Karolyi2Ana Kaić3Zoran Luković4Divison of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, CroatiaFamily Enterprise Jančo, Matije Gupca 19, 31424 Punitovci, CroatiaDivison of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, CroatiaDivison of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, CroatiaDivison of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska cesta 25, 10000 Zagreb, CroatiaManagement of hyperprolific sows is challenging when it comes to controlling birth weight variability and piglet survival in large litters. The growth of low birth weight piglets can be compromised and have a negative impact on production efficiency. The objective of the study was to apply a random regression coefficient model to estimate the main effects of the growth of piglets of highly prolific sows. The dataset contained growth data for 360 piglets from 25 Pen Ar Lan Naima sows. In addition to routine procedures after farrowing, piglets were weighed five times: on day 1 after farrowing, on day 14 of life, at weaning on day 28, on day 30 of nursery period, and at the end of the nursery period when piglets were 83 days old. Data were treated as longitudinal, with body weight as the dependent variable. Fitting age as a quadratic regression within piglets in the random part of the model helped to determine the significant effect of birth weight, litter size, and parity on the growth of the piglets. Since the piglets from large litters often have non-uniform birth weights and this can affect further growth, the use of a random regression coefficient model is practical for analysing the growth of such piglets due to the ability to describe the individual growth pattern of every individual.https://www.mdpi.com/2076-2615/13/18/2888hyperprolific sowsbirth weightgrowthrandom regression coefficient |
| spellingShingle | Dubravko Škorput Nina Jančo Danijel Karolyi Ana Kaić Zoran Luković Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient Animals hyperprolific sows birth weight growth random regression coefficient |
| title | Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient |
| title_full | Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient |
| title_fullStr | Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient |
| title_full_unstemmed | Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient |
| title_short | Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient |
| title_sort | analysis of early growth of piglets from hyperprolific sows using random regression coefficient |
| topic | hyperprolific sows birth weight growth random regression coefficient |
| url | https://www.mdpi.com/2076-2615/13/18/2888 |
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