Training and Adaptation of Beef Calves to Precision Supplementation Technology for Individual Supplementation in Grazing Systems

Abstract

Supplementation of beef cattle can be used to meet both nutrient requirements and production goals; however, supplementation costs influence farm profitability. Common supplementation delivery strategies are generally designed to provide nutrients to the mean of the group instead of an individual. Precision individual supplementation technologies, such as the Super SmartFeed (SSF, C-Lock Inc., Rapid City, SD, USA), are available but are generally cost prohibitive to producers. These systems require adaptation or training periods for cattle to utilize this technology. The objective of this research was to assess the training and adoption rates of three different groups of cattle (suckling calves, weaned steers, replacement heifers) to the SSF. Successful adaptation was determined if an individual’s supplement intake was above the group average of total allotted feed consumed throughout the training period. Suckling calves (n = 31) underwent a 12 d training period on pasture; 45% of suckling calves adapted to the SSF and average daily intake differed (p < 0.0001) by day of training. Weaned steers (n = 79) were trained in drylot for 13 d. Of the weaned steers, 62% were trained to the SSF, and average daily intake differed (p < 0.0001) by day of training. Replacement heifers (n = 63) grazed tall fescue pastures and had access to SSF for 22 d of training. The success rate of replacement heifers was 73%. For replacement heifers, the daily intake did not differ (p < 0.0001) by day of training. Results indicate production stage may influence cattle adaptation to precision technologies.

Development of an application programming interface to automate downloading and processing of precision livestock data

“…Though animal scientists are well trained in research and statistical design of experiments, they are not trained in data science principles and may lack the necessary skills to tackle many of the big data challenges associated with precision technology ( Jacobs et al, 2022 ). These skills include downloading large datasets that are generated daily, creating data pipelines to automate processing and cleaning of precision data, generating visualizations to assess quality issues, identifying individual animal non-adoption with precision technology, and incorporating data into animal nutrition or machine learning models ( Brennan et al, 2023a ; Dagel, 2023 ; Jacobs et al, 2023 ; Parsons et al, 2023 ). The time required to clean, process, and manage large and complex data can require up to 80% of the work effort on a project ( Light et al, 2014 ).…”

Field Implementation of Precision Livestock Farming: Selected Proceedings from the 2nd U.S. Precision Livestock Farming Conference

“…The USPLF2023 conference featured four primary research topics: Sensors and Sensing in PLF [1][2][3][4][5], Data Management and Algorithm Development [6][7][8], Measuring, Modeling, and Managing of Dynamic Responses [9][10][11][12][13][14], and Societal Impacts of PLF [15,16]. A total of 126 submissions were received for these topics from individuals representing universities, research institutions, and PLF companies from 13 different countries across Africa, Asia, Australia, Europe, North America, and South America.…”