哺乳母猪智能饲喂系统设计与试验

doi:10.13733/j.jcam.issn.2095-5553.2023.07.021

摘要/Abstract

摘要： Bus总线的哺乳母猪智能精准饲喂系统，可在现场方便进行猪只出入栏管理、猪只状态动态监测、异常情况实时报警功能。在云南省某规模化母猪场安装280台哺乳母猪智能饲喂器，与配置传统饲喂的生产线进行生产性能对比试验。试验结果表明，智能饲喂实现人机交互友好、触碰下料、智能湿拌、无接触操作和易检修的特点。智能饲喂较传统饲喂相比，可提高母猪平均日采食量0.673kg，日饲料浪费量减少0.345kg，智能饲喂和传统饲喂不同日龄的平均日采食量和日饲料浪费量存在极显著差异（P<0.01）；月产健仔数提高10.44%，窝均健仔数增加0.69头，21天断奶仔猪均重增加0.370kg；哺乳母猪背膘损失降低34.43%，7天断配率提高4%。对系统进行投资回报率分析，智能饲喂能有效减少饲料浪费，提高生产成绩，投资回报率高，可有效助力规模化猪场降本增效。该研究对母猪智能饲喂系统的开发和应用提供参考。

关键词: 哺乳母猪, 智能饲喂, 生产性能, 采食量, 断配率

Abstract: To achieve intelligent feeding, userfriendly humancomputer interaction, and easy maintenance for lactating sows, a set of intelligent and precise feeding systems for lactating sows was designed. This system includes an intelligent feeder, a handheld personal digital assistant terminal, a central controller, and a CANBus bus. It enables convenient onsite management of pig entry and exit management, dynamic monitoring of pig status, and realtime alarm functions for abnormal conditions. In a largescale sow farm in Yunnan Province, 280 intelligent feeders for lactating sows were installed, and a production performance comparison test was carried out with the production line equipped with traditional feeding. The test results showed that intelligent feeding facilitates realized userfriendly humancomputer interaction, touchsensitive feeding, intelligent wet mixing, noncontact operation, and easy maintenance. Compared with traditional feeding, intelligent feeding increased the average daily feed intake of sows by 0.673kg and reduced the daily feed waste by 0.345kg, indicating significant differences (P<0.01) in the average daily feed intake and daily feed waste at different ages between intelligent feeding and traditional feeding. The number of healthy piglets per month increased by 10.44%, the number of healthy piglets per litter increased by 0.69, and the average weight of weaned piglets at 21 days increased by 0.370kg. The backfat loss of lactating sows decreased by 34.43%, and the 7-day weaning estrus rate increased by 4%. The return on investment analysis of the system demonstrated that intelligent feeding effectively reduces feed waste, improves production performance, and has a high return on investment, which can effectively help largescale pig farms reduce costs. This study provides a reference for the development and application of intelligent feeding systems for sows.

Key words: lactating sows, intelligent feeding, production performance, feed intake, weaning estrus rate

中图分类号:

S818.9： S828

吕恩利, 陈高峰, , 夏晶晶, , 王飞仁, 吴凡, , 曾志雄, . 哺乳母猪智能饲喂系统设计与试验[J]. 中国农机化学报, 2023, 44(7): 154-162.

Lü Enli, Chen Gaofeng, , Xia Jingjing, , Wang Feiren, Wu Fan, , Zeng Zhixiong, . Design and test of intelligent feeding system for lactating sows[J]. Journal of Chinese Agricultural Mechanization, 2023, 44(7): 154-162.

参考文献

［1］　孟蕊, 崔晓东, 余礼根, 等. 畜禽精准饲喂管理技术发展现状与展望［J］. 家畜生态学报, 2021, 42（2）: 1-7.
Meng Rui, Cui Xiaodong, Yu Ligen, et al. The development status and prospects of livestock and poultry precise feeding management technologies ［J］. Journal of Domestic Animal Ecology, 2021, 42（2）: 1-7.
［2］　Normile D. Arrival of deadly pig disease could spell disaster for China ［J］. Science, 2018, 361: 741.
［3］　Stokstad E. Deadly virus threatens European pigs and boar ［J］. Science, 2017, 358: 1516-1517.
［4］　郭瑶, 姚春燕, 王强军, 等. 智能电子饲喂系统下猪采食时间对行为节律和生长的影响［J］. 农业工程学报, 2022, 38（11）: 206-214.
Guo Yao, Yao Chunyan, Wang Qiangjun, et al. Effects of eating time on behavioral rhythm and performance of pigs under an intelligent electronic feeding system ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38（11）: 206-214.
［5］　黎煊, 帅永辉, 刘小磊, 等. 自锁式小群妊娠母猪智能饲喂机构设计与试验［J］. 农业工程学报, 2022, 38（13）: 38-46.
Li Xuan, Shuai Yonghui, Liu Xiaolei, et al. Mechanism design and experiments of the selflocking intelligent feeding system for smallgroup pregnant sows ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38（13）: 38-46.
［6］　熊本海, 杨亮, 郑姗姗, 等. 哺乳母猪精准饲喂下料控制系统的设计与试验［J］. 农业工程学报, 2017, 33（20）: 177-182.
Xiong Benhai, Yang Liang, Zheng Shanshan, et al. Design and test of precise blanking control system for lactating sows ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33（20）: 177-182.
［7］　王美芝, 安涛, 刘继军, 等. 智能饲喂器对哺乳母猪采食量体况和生产性能的影响［J］. 农业工程学报, 2019, 35（6）: 190-197.
Wang Meizhi, An Tao, Liu Jijun, et al. Effect of intelligent feeder on feed intake, body condition and production performance of lactating sows ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35（6）: 190-197.
［8］　刘妍华, 周思理, 曹永峰, 等. 妊娠母猪智能饲喂器设计与试验［J］. 中国农机化学报, 2021, 42（4）: 183-189.
Liu Yanhua, Zhou Sili, Cao Yongfeng, et al. Design and testing of intelligent feeder for pregnant sows ［J］. Journal of Chinese Agricutural Mechanization, 2021, 42（4）: 183-189.
［9］　刘俊灵, 石军锋. 智能母猪饲喂系统的高效可靠数据通信协议设计［J］. 中国农机化学报, 2020, 41（8）: 185-190, 210.
Liu Junling, Shi Junfeng.Design of efficient and reliable data communication protocol for intelligent sows feeding system ［J］. Journal of Chinese Agricultural Mechanization, 2020, 41（8）: 185-190, 210.
［10］　Gauthier R, Largout C, Rozé L, et al. Online forecasting of daily feed intake in lactating sows supported by offline timeseries clustering, for precision livestock farming ［J］. Computers and Electronics in Agriculture, 2021, 188: 106329.
［11］　Thomas L L, Herd L K, Goodband R D, et al. Effects of increasing standardized ileal digestible lysine during gestation on reproductive performance of gilts and sows ［J］. Animal, 2021, 15（7）: 100221.
［12］　Gaillard C, Durand M, Largout C, et al. Effects of the environment and animal behavior on nutrient requirements for gestating sows: Future improvements in precision feeding ［J］. Animal Feed Science and Technology, 2021, 279: 115034.
［13］　Ma Weihong, Fan Jinwei, Zhao Chunjiang, et al. The realization of pig intelligent feeding equipment and network service platform ［C］. Proceedings of the International Conference on Intelligent Agriculture 2017 （ICIA2017） Part II, 2017.
［14］　梁逸夫. 母猪批次化管理和精准饲喂技术研究［D］. 武汉: 华中农业大学, 2022.
Liang Yifu. Precision feeding technology studies on sow batch management and precision feeding technology ［D］. Wuhan: Huazhong Agricultural University, 2022.
［15］　曹永峰, 陈高峰, 王飞仁, 等. 保育猪智能粥料饲喂系统设计与试验［J］. 中国农机化学报, 2022, 43（7）: 36-43.
Cao Yongfeng, Chen Gaofeng, Wang Feiren, et al. Design and experiment of intelligent feeding system for piglets ［J］. Journal of Chinese Agricultural Mechanization, 2022, 43（7）: 36-43.
［16］　赵春江. 智慧农业的发展现状与未来展望［J］. 华南农业大学学报, 2021, 42（6）: 1-7.
Zhao Chunjiang. Current situations and prospects of smart agriculture ［J］. Journal of South China Agricultural University, 2021, 42（6）: 1-7.
［17］　丁友强, 刘彦伟, 杨丽, 等. 基于Android和CAN总线的玉米播种机监控系统研究［J］. 农业机械学报, 2019, 50（12）: 33-41.
Ding Youqiang, Liu Yanwei, Yang Li, et al. Monitoring system of maize precision planter based on Android and CAN bus ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50（12）: 33-41.
［18］　Juul L, Kristensen T, Theil P K, et al. Effect of two different feeding strategies on energy intake from pasture, feed efficiency and growth performance of growingfinishing pigs in a mobile pasture system ［J］. Livestock Science, 2021, 252: 104690.
［19］　Xin H, Wang M, Xia Z, et al. Fermented diet liquid feeding improves growth performance and intestinal function of pigs ［J］. Animals （Basel）, 2021, 11（5）.
［20］　刘莫尘, 马郡祥, 韩守强, 等. 固态发酵饲料自动发酵饲喂一体设备设计与试验［J］. 农业机械学报, 2018, 49（11）: 141-147.
Liu Mochen, Ma Junxiang, Han Shouqiang, et al. Design and experiment of automatic fermenting and feeding equipment for solidstate fermented feed ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49（11）: 141-147.
［21］　吕恩利, 何欣源, 罗毅智, 等. 哺乳母猪智能饲喂物联网系统设计［J］. 华南农业大学学报, 2023, 44（1）: 57-64.
Lü Enli, He Xinyuan, Luo Yizhi, et al. Design of intelligent feeding IoT system for lactating sows ［J］. Journal of South China Agricultural University, 2023, 44（1）: 57-64.
［22］　Gaillard C, Brossard L, Dourmad J Y. Improvement of feed and nutrient efficiency in pig production through precision feeding ［J］. Animal Feed Science and Technology, 2020, 268: 114611.
［23］　Pierozan C R, Callegari M A, Dias C P, et al. Herdlevel factors associated with piglet weight at weaning, kilograms of piglets weaned per sow per year and sow feed conversion ［J］. Animal, 2020, 14（6）: 1283-1292.
［24］　Clowes E J, Aherne F X, Foxcroft G R, et al. Selective protein loss in lactating sows is associated with reduced litter growth and ovarian function ［J］. Journal of Animal Science, 2003, 81（3）: 753-764.
［25］　Gianluppi R D F, Lucca M S, Mellagi A P G, et al. Effects of different amounts and type of diet during weaningtoestrus interval on reproductive performance of primiparous and multiparous sows ［J］. Animal, 2020, 14（9）: 1906-1915.
［26］　李煜, 黄程, 林五清, 等. 长大初产母猪背膘厚度对繁殖性能的影响［J］. 华中农业大学学报, 2020, 39（1）: 111-115.
Li Yu, Huang Cheng, Lin Wuqing, et al. Effect of backfat thickness on reproductive performance in Landrace × Yorkshire gilt ［J］. Journal of Huazhong Agricultural University, 2020, 39（1）: 111-115.
［27］　Lavery A, Lawlor P G, Magowan E, et al. An association analysis of sow parity, liveweight and backfat depth as indicators of sow productivity ［J］. Animal, 2019, 13（3）: 622-630.
［28］　Jang J C, Jung W S, Jin S S, et al. The effects of gilts housed either in group with the electronic sow feeding system or conventional stall ［J］. Asian Australas Journal of Animal Sciences, 2015, 28（10）: 1512-1518.
［29］　Ali B M, Berentsen P B M, Bastiaansen J W M, et al. A stochastic bioeconomic pig farm model to assess the impact of innovations on farm performance ［J］. Animal, 2018, 12（4）: 819-830.
［30］　Gaillard C, Dourmad J. Application of a precision feeding strategy for gestating sows ［J］. Animal Feed Science and Technology, 2022, 287: 115280.

[1]	杨亮, 龚进牧, 王辉, 赵一广, 熊本海. 育肥猪精准下料控制系统设计与验证[J]. 中国农机化学报, 2022, 43(7): 26-30.
[2]	曹永峰, 陈高峰, , 王飞仁, 曾志雄, 吕恩利, 郭嘉明. 保育猪智能粥料饲喂系统设计与试验[J]. 中国农机化学报, 2022, 43(7): 36-43.
[3]	刘妍华;周思理;曹永峰;夏晶晶;吴凡;曾志雄;. 妊娠母猪智能饲喂器设计与试验[J]. 中国农机化学报, 2021, 42(4): 183-189+204.
[4]	席瑞谦;王娟;李正义;周棕凯;. 奶牛智能饲喂关键技术研究[J]. 中国农机化学报, 2021, 42(2): 190-196.
[5]	沈启扬, 雷哓晖, 马拯胞, 李健, . F.US-UFO型果园避障割草机试验研究[J]. 中国农机化学报, 2021, 42(10): 65-71+77.