Novel insight into the feed conversion ratio in laying hens and construction of its prediction model

文献类型： 外文期刊

第一作者： Li, Yan

作者： Li, Yan;Ma, Ruiyu;Qi, Renrong;Li, Junying;Liu, Wei;Wan, Yi;Li, Sanjun;Zhan, Kai;Li, Hualong;Li, Junying;Xu, Jiechi

作者机构： Anhui Acad Agr Sci, Inst Anim Husb & Vet Med, Anhui Prov Key Lab Livestock & Poultry Prod Safety, Hefei 230031, Anhui, Peoples R China;Chinese Acad Sci, Hefei Inst Phys Sci, Adv Mfg Technol Res Ctr Inst Intelligent Machines, Hefei 230001, Peoples R China;Anhui Agr Univ, Coll Anim Sci & Technol, Hefei 230036, Anhui, Peoples R China;Qianshan Tiansheng Agr Ecol Technol Dev Co Ltd, Qianshan 246300, Anhui, Peoples R China

关键词： feed conversion ratio; laying hen; molecular mechanism; environmental variable; prediction model

期刊名称：POULTRY SCIENCE （ 2023影响因子：3.8； 五年影响因子：4.1 ）

ISSN： 0032-5791

年卷期： 2024 年 103 卷 10 期

页码：

收录情况： SCI

摘要： Feed efficiency (FE) is an important economic factor in poultry production, and feed conversion ratio (FCR) is one of the most widely used measures of FE. Factors associated with FCR include genetics, the environment, and other factors. However, the mechanisms responsible for FCR in chickens are still less well appreciated. In this study, we examined the pattern changes of FCR, then delved into understanding the mechanisms behind these variations from both genetic and environmental perspectives. Most interestingly, the FCR at the front section of henhouse exhibited the lowest value. Further investigation revealed that laying rate in the high FCR (HFCR) group was lower than that in the low FCR (LFCR) group (P < 0.05). Cortisol, total antioxidant capacity (TAOC), and IgG levels in the LFCR group were significantly lower than those in the HFCR group (P < 0.05), while BUN level was significantly higher than that in the HFCR group (P < 0.05). We identified a total of 67 and 10 differentially expressed genes (DEGs) associated with FCR in ovarian and small intestine tissues, respectively. Functional enrichment analysis of DEGs revealed that they might affect FCR by modulating genes associated with salivary secretion, ferroptosis, and mineral absorption. Moreover, values for relative humidity (RH), air velocity (AV), PM2.5, ammonia (NH3), and carbon dioxide (CO2) in the LFCR group were significantly lower than those in the HFCR group (P < 0.05). Conversely, value for light intensity (LI) in the LFCR group was significantly higher than that in the HFCR group (P < 0.05). Correlation analysis revealed a positive correlation between FCR and RH, AV, PM2.5, NH3, and CO2, and a negative correlation with LI. Finally, the FCR prediction model was successfully constructed based on multiple environmental variables using the random forest algorithm, providing a valuable tool for predicting FCR in chickens.

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