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Application of Different Rearing Modes on Growth Performance of Goslings in Cold Northern Regions Under the Background of Artificial Intelligence
Research Article  ·  Published: 09 March 2026
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Digital Intelligence in Agriculture
Volume 2, Issue 1, 2026: 1-11
Research Article Open Access

Application of Different Rearing Modes on Growth Performance of Goslings in Cold Northern Regions Under the Background of Artificial Intelligence

by
Hongxi Xu Hongxi Xu 1 ORCID
Meimei Zhu Meimei Zhu 2 ORCID
Min Zeng Min Zeng 1 ORCID
Hongfeng Wu Hongfeng Wu 2 ORCID
Yan Fu Yan Fu 3 ORCID
Xiaojing Zhao Xiaojing Zhao 2 ORCID
Chunqin Wu Chunqin Wu 1 ORCID
Hongliang Zhang Hongliang Zhang 2 * ORCID
1 Wenzhou Vocational College of Science and Technology, Wenzhou 325006, China
2 Heilongjiang Academy of Agricultural Reclamation Sciences, Harbin 150036, China
3 Heilongjiang Agricultural Reclamation Vocational College, Harbin 150025, China
Corresponding Author: Hongliang Zhang, [email protected]
Volume 2, Issue 1
Volume 2, Issue 1 2026
Received: 24 July 2025 Accepted: 06 March 2026 Published: 09 March 2026 CrossMark
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DOI: 10.62762/DIA.2025.864705
ARK: ark:/57805/dia.2025.864705

Article Information

Published in Digital Intelligence in Agriculture
Volume/Issue Volume 2, Issue 1, 2026
Pages 1-11

Abstract

Under the background of AI promoting precision livestock farming, this study compared the effects of fermentation bed and net bed systems on gosling rearing in cold northern regions using AI-based intelligent temperature monitoring. A total of 10,000 one-day-old "Dasanhua" goslings were divided into two groups (n=5,000/group) and reared for 28 days. An AI-driven wireless temperature sensing system enabled real-time, high-precision monitoring of environmental temperatures. Growth performance (ADG, ADFI, F/G, survival rate) and economic benefits were systematically evaluated. The AI system revealed significant temperature differences: the fermentation bed maintained a stable average temperature of 30.9±0.6°C (fluctuation range 2.1°C), staying within the optimal brooding range (28–32°C) for 98% of the time. In contrast, the net bed averaged 22.5±1.0°C with a wider fluctuation of 4.8°C, meeting optimal conditions only 72% of the time (P<0.05). The stable thermal environment of the fermentation bed significantly improved growth performance, with 16% higher ADG (50.8 vs. 42.5 g/bird/day), 11% lower F/G (1.98 vs. 2.2), and 2% higher survival rate (98% vs. 96%) (P<0.05). Economically, despite higher feed costs (4,000 yuan) and AI system amortization (200 yuan), the fermentation bed saved 3,000 yuan in electricity and 2,000 yuan in bedding costs, yielding an additional profit of 2,000 yuan for 5,000 goslings. In conclusion, integrating fermentation bed rearing with AI-based temperature monitoring provides a stable, data-driven microenvironment for gosling brooding in cold regions. This approach significantly enhances growth performance and economic returns, offering a viable pathway for intelligent and sustainable waterfowl farming.

Keywords

artificial intelligence intelligent temperature monitoring gosling fermentation bed cold northern region growth performance precision livestock farming

Data Availability Statement

Data will be made available on request.

Funding

This work was supported in part by the Wenzhou New Poultry Variety Breeding Cooperation Group Project under Grant 2019ZX005; in part by the Sub-project of the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA28100401; in part by the Cangnan County Modern Agricultural Industry Enhancement Project under Grant 2024CNYJY08.

Conflicts of Interest

The authors declare no conflicts of interest. 

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

This study did not require formal ethical approval because the host institution lacks an animal care and use committee, and the experiment consisted of standard commercial gosling brooding practices without procedures that could cause pain, distress, or lasting harm. Animal care followed best practices for waterfowl farming and the 3Rs principles.

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APA Style
Xu, H., Zhu, M., Zeng, M., Wu, H., Fu, Y., Zhao, X., Wu, C., & Zhang, H. (2026). Application of Different Rearing Modes on Growth Performance of Goslings in Cold Northern Regions Under the Background of Artificial Intelligence. Digital Intelligence in Agriculture, 2(1), 1–11. https://doi.org/10.62762/DIA.2025.864705
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TY  - JOUR
AU  - Xu, Hongxi
AU  - Zhu, Meimei
AU  - Zeng, Min
AU  - Wu, Hongfeng
AU  - Fu, Yan
AU  - Zhao, Xiaojing
AU  - Wu, Chunqin
AU  - Zhang, Hongliang
PY  - 2026
DA  - 2026/03/09
TI  - Application of Different Rearing Modes on Growth Performance of Goslings in Cold Northern Regions Under the Background of Artificial Intelligence
JO  - Digital Intelligence in Agriculture
T2  - Digital Intelligence in Agriculture
JF  - Digital Intelligence in Agriculture
VL  - 2
IS  - 1
SP  - 1
EP  - 11
DO  - 10.62762/DIA.2025.864705
UR  - https://www.icck.org/article/abs/DIA.2025.864705
KW  - artificial intelligence
KW  - intelligent temperature monitoring
KW  - gosling
KW  - fermentation bed
KW  - cold northern region
KW  - growth performance
KW  - precision livestock farming
AB  - Under the background of AI promoting precision livestock farming, this study compared the effects of fermentation bed and net bed systems on gosling rearing in cold northern regions using AI-based intelligent temperature monitoring. A total of 10,000 one-day-old "Dasanhua" goslings were divided into two groups (n=5,000/group) and reared for 28 days. An AI-driven wireless temperature sensing system enabled real-time, high-precision monitoring of environmental temperatures. Growth performance (ADG, ADFI, F/G, survival rate) and economic benefits were systematically evaluated. The AI system revealed significant temperature differences: the fermentation bed maintained a stable average temperature of 30.9±0.6°C (fluctuation range 2.1°C), staying within the optimal brooding range (28–32°C) for 98% of the time. In contrast, the net bed averaged 22.5±1.0°C with a wider fluctuation of 4.8°C, meeting optimal conditions only 72% of the time (P
SN  - 3069-3187
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
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@article{Xu2026Applicatio,
  author = {Hongxi Xu and Meimei Zhu and Min Zeng and Hongfeng Wu and Yan Fu and Xiaojing Zhao and Chunqin Wu and Hongliang Zhang},
  title = {Application of Different Rearing Modes on Growth Performance of Goslings in Cold Northern Regions Under the Background of Artificial Intelligence},
  journal = {Digital Intelligence in Agriculture},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {1-11},
  doi = {10.62762/DIA.2025.864705},
  url = {https://www.icck.org/article/abs/DIA.2025.864705},
  abstract = {Under the background of AI promoting precision livestock farming, this study compared the effects of fermentation bed and net bed systems on gosling rearing in cold northern regions using AI-based intelligent temperature monitoring. A total of 10,000 one-day-old "Dasanhua" goslings were divided into two groups (n=5,000/group) and reared for 28 days. An AI-driven wireless temperature sensing system enabled real-time, high-precision monitoring of environmental temperatures. Growth performance (ADG, ADFI, F/G, survival rate) and economic benefits were systematically evaluated. The AI system revealed significant temperature differences: the fermentation bed maintained a stable average temperature of 30.9±0.6°C (fluctuation range 2.1°C), staying within the optimal brooding range (28–32°C) for 98\% of the time. In contrast, the net bed averaged 22.5±1.0°C with a wider fluctuation of 4.8°C, meeting optimal conditions only 72\% of the time (P},
  keywords = {artificial intelligence, intelligent temperature monitoring, gosling, fermentation bed, cold northern region, growth performance, precision livestock farming},
  issn = {3069-3187},
  publisher = {Institute of Central Computation and Knowledge}
}

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