Silvi Rebeca, Pereira Luiz Gustavo R, Paiva Claudio Antônio V, Tomich Thierry R, Teixeira Vanessa A, Sacramento João Paulo, Ferreira Rafael E P, Coelho Sandra G, Machado Fernanda S, Campos Mariana M, Dórea João Ricardo R
Department of Agricultural and Environmental Sciences, Universidade Estadual de Santa Cruz, Ilhéus 45662-900, BA, Brazil.
Brazilian Agricultural Research Corporation-Embrapa Dairy Cattle, Juiz de Fora 36038-330, MG, Brazil.
Animals (Basel). 2021 Dec 7;11(12):3488. doi: 10.3390/ani11123488.
The use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest in farming technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. The objective of this study was to characterize Brazilian dairy farms based on technology usage. Factors such as willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes were investigated in 378 dairy farms located in Brazil. A survey with 22 questions was developed and distributed via Google Forms from July 2018 to July 2020. The farms were then classified into seven clusters: (1) top yield farms; (2) medium-high yield, medium-tech; (3) medium yield and top high-tech; (4) medium yield and medium-tech; (5) young medium-low yield and low-tech; (6) elderly medium-low yield and low-tech; and (7) low-tech grazing. The most frequent technologies adopted by producers were milk meters systems (31.7%), milking parlor smart gate (14.5%), sensor systems to detect mastitis (8.4%), cow activity meter (7.1%), and body temperature (7.9%). Based on a scale containing numerical values (1-5), producers indicated "available technical support" (mean; σ) (4.55; 0.80) as the most important decision criterion involved in adopting technology, followed by "return on investment-ROI" (4.48; 0.80), "user-friendliness" (4.39; 0.88), "upfront investment cost" (4.36; 0.81), and "compatibility with farm management software" (4.2; 1.02). The most important factors precluding investment in precision dairy technologies were the need for investment in other sectors of the farm (36%), the uncertainty of ROI (24%), and lack of integration with other farm systems and software (11%). Farmers indicated that the most useful technologies were automatic milk meters systems (mean; σ) (4.05; 1.66), sensor systems for mastitis detection (4.00; 1.57), automatic feeding systems (3.50; 2.05), cow activity meter (3.45; 1.95), and in-line milk analyzers (3.45; 1.95). Overall, the concerns related to data integration, ROI, and user-friendliness of technologies are similar to those of dairy farms located in other countries. Increasing available technical support for sensing technology can have a positive impact on technology adoption.
在过去几年中，诸如挤奶机器人、自动犊牛饲养器、可穿戴传感器等精准养殖技术在奶牛场的使用显著增加。在包括巴西在内的几个国家，人们对旨在减少劳动力、提高生产力和增加盈利能力的养殖技术的兴趣日益明显。有关奶牛场技术采用、认知和效果的信息可以揭示科学研究和推广项目需要解决的挑战。本研究的目的是根据技术使用情况对巴西奶牛场进行特征描述。在巴西的378个奶牛场中，调查了诸如对精准技术投资的意愿、传感器系统的采用情况、农民概况、农场特征和生产指标等因素。2018年7月至2020年7月期间，通过谷歌表单开展了一项包含22个问题的调查。这些农场随后被分为七个类别：(1)高产农场；(2)中高产、中等技术水平；(3)中产且高科技水平；(4)中产且中等技术水平；(5)年轻的中低产且低技术水平；(6)年长的中低产且低技术水平；以及(7)低技术水平放牧。生产者采用最频繁的技术是奶量计系统(31.7%)、挤奶厅智能门(14.5%)、乳腺炎检测传感器系统(8.4%)、奶牛活动计(7.1%)和体温检测(7.9%)。根据一个包含数值(1 - 5)的量表，生产者表示“可获得的技术支持”(均值；标准差)(4.55；0.80)是采用技术时最重要的决策标准，其次是“投资回报率 - ROI”(4.48；0.80)、“用户友好性”(4.39；0.88)、“前期投资成本”(4.36；0.81)以及“与农场管理软件的兼容性”(4.2；1.02)。阻碍对精准奶牛技术投资的最重要因素是农场其他部门需要投资(36%)、投资回报率的不确定性(24%)以及与其他农场系统和软件缺乏整合(11%)。农民表示最有用的技术是自动奶量计系统(均值；标准差)(4.05；1.66)、乳腺炎检测传感器系统(4.00；1.57)、自动饲喂系统(3.50；2.
