Introduction Precision livestock farming (PLF) technology development has proliferated recently, but on-farm adoption has lagged. Understanding PLF stakeholders’ views, practical applications, limitations, and concerns are necessary to understand the factors influencing the adoption of PLF technology. Methods Using semi-structured online interviews, 12 influential stakeholders’ PLF views and lived experiences were investigated. A phenomenological paradigm was used to generate qualitative data that was analyzed using template analysis. Results We identified two unique groups of stakeholders, namely the PLF enthusiast and PLF cautious groups. The majority of the participants were well aware and had firsthand experience with the PLF technologies that are currently being used in the swine industry. Discussion We found that PLF technology was perceived to improve specificity in decision-making, better care for pigs, improve animal health and welfare, increase labor efficiency, and improve resource-use efficiency. Poor internet connectivity and the inability to use PLF data for decision-making due to needing to first complete daily on-farm tasks were considered key obstacles to its implementation. To integrate PLF technology into the existing barn environment, it is necessary to modify farm buildings and infrastructure and management protocols. Stakeholders’ main concerns with PLF technology included data privacy issues and the influence of PLF technology on human-animal relationships and farmers’ duty of care to the animals. In conclusion, stakeholders perceived camera systems for monitoring pig health and welfare and ensuring individual pig identification as a high priority in PLF development going forward.

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Precision livestock farming: a qualitative exploration of swine industry stakeholders

Agri-food 4.0: Drivers and links to innovation and eco-innovation

One of the problems of cattle breeders in terms of body weight growth not developing according to the target is the mechanism for maintaining cow health. One of the first treatments in the selection of cattle is the body temperature and heart rate of the cow must always be stable. A normal healthy body temperature is an average of 101.5 °F or 38.6 °C. As for the normal heart rate of cattle, it ranges from 55 times - 80 times/minute. In Indonesia, there is no technological innovation to monitor cattle growth and cow health in real-time and integrated with the Internet of Things (IoT). This paper proposes a tool called the Digital Smart Collar that can be used in real-time to monitor the health and development of cattle. This tool was developed using a combination of Sensor Pulse Oximetry, Sensor Temperature (MLX90614), Sensor Current and voltage (INA219), Module Microcontroller (ESP 32U), and Programming Arduino 1.8.13. The results of the trial by comparing the digital smart collar with a digital thermometer and stethoscope and then calculated using MAPE, the average MAPE value was 1.254 for measuring body temperature, and the average MAPE value was 2.434 for measuring heart rate. 

Digital Smart Collar: Monitoring Cow Health Using Internet of Things

Abstract Agriculture plays a central role in providing food security and essential goods globally. Producers must consider and manage risk to ensure that the production system and its associated individuals are capable of enduring unexpected and disruptive events. Analyzing the different types of risk and accompanying uncertainties that growers experience can be essential to better reflect and understand the realities of their circumstances, but these concepts are not always accounted for in the adoption process. Drawing on the importance of risk and uncertainty, this study aims to assess the different types of risk and uncertainties involved in the risk decision-making process of the processed raspberry industry, where plastic mulch is a new production technique. Semi-structured interviews were conducted with participants involved in the use, research, outreach, manufacturing, and distribution of plastic mulch, specifically polyethylene (PE) mulch and soil-biodegradable mulch (BDM). Findings indicate that risk can be present in various forms including production, price, and hidden risks, with production and price risks being the most significant to all participants. When accounting for overall risk, PE mulch was considered riskier to industry representatives but less risky to growers and most research and outreach specialists. BDM was considered risky due to the uncertainties about durability, degradability, and the unknown impacts on the environment if BDM fragments do not degrade readily. The application of PE mulch and/or BDM can be beneficial for the raspberry production systems but will require time for additional research and effort to disseminate information to a wider agricultural audience.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/2C3B6AA1E4842AE9A903AAFDC902D65C/S1742170522000291a.pdf/div-class-title-risk-and-uncertainty-of-plastic-mulch-adoption-in-raspberry-production-systems-div.pdf

Risk and uncertainty of plastic mulch adoption in raspberry production systems

This paper reports on the use of estimates of individual animal feed intake (made using time spent feeding measurements) to predict the Feed Conversion Ratio (FCR), a measure of the amount of feed consumed to produce 1 kg of body mass, for an individual animal. Reported research to date has evaluated the ability of statistical methods to predict daily feed intake based on measurements of time spent feeding measured using electronic feeding systems. The study collated data of the time spent eating for 80 beef animals over a 56-day period as the basis for the prediction of feed intake. A Support Vector Regression (SVR) model was trained to predict feed intake and the performance of the approach was quantified. Here, feed intake predictions are used to estimate individual FCR and use this information to categorise animals into three groups based on the estimated Feed Conversion Ratio value. Results provide evidence of the feasibility of utilising the ‘time spent eating’ data to estimate feed intake and in turn Feed Conversion Ratio (FCR), the latter providing insights that guide farmer decisions on the optimisation of production costs.

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Katharine M. Wood

Papers

Investigating perceptions, adoption, and use of digital technologies in the Canadian beef industry