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Genet Sel Evol ; 50(1): 46, 2018 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-30227828


BACKGROUND: In polytocous livestock species, litter size and offspring weight act antagonistically; in modern pig breeds, selection for increased litter size has resulted in lower mean birth weights, an increased number of small piglets and an increased number of those affected by varying degrees of intrauterine growth retardation (IUGR). IUGR poses life-long challenges, both mental, with morphological brain changes and altered cognition, and physical, such as immaturity of organs, reduced colostrum intake and weight gain. In pigs, head morphology of newborn piglets is a good phenotypic marker for identifying such compromised piglets. Growth retardation could be considered as a property of the dam, in part due to either uterine capacity or insufficiency. A novel approach to this issue is to consider the proportion of IUGR-affected piglets in a litter as an indirect measure of uterine capacity. However, uterine capacity or sufficiency cannot be equated solely to litter size and thus is a trait difficult to measure on farm. RESULTS: A total of 21,159 Landrace × Large White or Landrace × White Duroc piglets (born over 52 weeks) with recorded head morphology and birth weights were followed from birth until death or weaning. At the piglet level, the estimated heritability for IUGR (as defined by head morphology) was low at 0.01 ± 0.01. Piglet direct genetic effects of birth weight (h2 = 0.07 ± 0.02) were strongly negatively correlated with head morphology (- 0.93), in that IUGR-affected piglets tended to have lower birth weights. At the sow level, analysis of the proportion of IUGR-affected piglets in a litter gave a heritability of 0.20 ± 0.06, with high and negative genetic correlations of the proportion of IUGR-affected piglets with average offspring birth weight (- 0.90) and with the proportion of piglets surviving until 24 h (- 0.80). CONCLUSIONS: This suggests that the proportion of IUGR-affected piglets in a litter is a suitable indirect measure of uterine capacity for inclusion in breeding programmes that aim at reducing IUGR in piglets and improving piglet survival.

Retardo do Crescimento Fetal/genética , Seleção Genética , Seleção Artificial , Doenças dos Suínos/genética , Suínos/genética , Animais , Peso ao Nascer , Feminino , Retardo do Crescimento Fetal/veterinária , Masculino , Característica Quantitativa Herdável , Suínos/embriologia , Útero/fisiologia
Comput Electron Agric ; 127: 521-530, 2016 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-27667883


This paper presents a novel approach to automated classification and quantification of sow postures and posture transitions that may enable large scale and accurate continuous behaviour assessment on farm. Automatic classification and quantification of postures and posture transitions in domestic animals has substantial potential to enhance their welfare and productivity. Analysis of such behaviours in farrowing sows can highlight the need for human intervention or lead to the prediction of movement patterns that are potentially dangerous for their piglets, such as crushing when the sow lies down. Data were recorded by a tri-axial accelerometer secured to the hind-end of each sow, in a deployment that involved six sows over the period around parturition. The posture state (standing, sitting, lateral and sternal lying) was automatically classified for the full dataset with a mean F1 score (a measure of predictive performance between 0 and 1) of 0.78. Sitting was shown to present a greater challenge to classification with a F1 score of 0.54, compared to the lateral lying postures, which were classified with an average F1 score of 0.91. Posture transitions were detected with a F1 score of 0.79. We automatically extracted and visualized a range of features that characterise the manner in which the sows changed posture in order to provide comparative descriptors of sow activity and lying style that can be used to assess the influence of genetics or housing design. The methodology presented in this paper can be applied in large scale deployments with potential for enhancing animal welfare and productivity on farm.