Data-driven modeling of reproductive performance: a cohort study for elevated sow efficiency and sustainability in livestock farming

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成果类型：

期刊论文

作者：

Su, Jiayi;Xie, Qian;Deng, Yuankun;Wang, Chengming;Xie, Shuai;...

通讯作者：

Yin, Yulong;Tan, Bie;Wang, J

作者机构：

[Gao, Ning; Tan, Bie; Wang, Jing; Su, Jiayi; Deng, Yuankun; Xie, Qian; Ma, Xiaokang; Wang, Chengming] Hunan Agr Univ, Coll Anim Sci & Technol, Key Lab Hunan Prov Prod Qual Regulat Livestock & P, Changsha 410128, Peoples R China.

[Xie, Shuai] Wuhan Huada Inst Life Sci, Wuhan 430000, Peoples R China.

[Kim, Sung Woo] North Carolina State Univ, Dept Anim Sci, Raleigh, NC 27695 USA.

[Nyachoti, Charles Martin] Univ Manitoba, Dept Anim Sci, Winnipeg, MB R3T 2N2, Canada.

[Yin, Yulong] Chinese Acad Sci, Lab Anim Nutr Physiol & Metab Proc, Key Lab Agroecol Proc Subtrop Reg, Inst Subtrop Agr,Natl Engn Lab Pollut Control & Wa, Changsha 410125, Peoples R China.

通讯机构：

[Wang, J ; Tan, B; Yin, YL] Y

Yuelushan Lab, Changsha 410128, Peoples R China.

语种：

英文

关键词：

Precision feeding;Artificial intelligence;Gradient boosted decision trees;SHapley additive exPlanations;Sow;Reproductive performance

期刊：

Computers and Electronics in Agriculture

ISSN：

0168-1699

年：

2025

卷：

237

页码：

110641

DOI：

10.1016/j.compag.2025.110641

基金类别：

National Key Research and Development Program of China [2021YFD1300401, 2022YFD1300403, 2021YFD1301004, 2021YFD1301005]; National Natural Science Foundation of China [U20A2054, U22A20510, 32072745, 32102571, 32130099]; Excellent Youth Foundation of Hunan Province of China [2022JJ20027]; Earmarked Fund for China Agriculture Research System [CARS-35]

机构署名：

本校为第一且通讯机构

院系归属：

动物科学技术学院

摘要：

The slow development of the Internet of Things (IoT) in pig production, due to the lack of high-quality data, limited large-scale models, and low hardware coverage, has hindered the widespread adoption of precision feeding practices. This study aimed to address these challenges by providing a standardized dataset as a foundation for IoT development and constructing predictive models focused on birth litter weight (BLW) and weaned litter weight (WLW). To achieve these objectives, two comprehensive datasets consisting of 10,089 sow characteristic...

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Data-driven modeling of reproductive performance: a cohort study for elevated sow efficiency and sustainability in livestock farming

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