Design, implementation and evaluation of precision feeding in dairy calves using automated sensors

Thesis Abstract

The dairy industry faces rising feed costs and environmental scrutiny, with current standard feeding practices often failing to optimize growth, health, and nutrient efficiency in neonatal calves. Precision feeding using automated sensors promises to tailor nutrient delivery to individual calves, potentially reducing waste, improving weaning performance, and enhancing immune resilience, yet empirical evidence on practical implementation, animal welfare implications, and economic viability remains fragmented. This study aims to design, implement, and evaluate a farm-scale precision-feeding system for dairy calves that leverages automated sensors to monitor intake, body weight, rumen development proxies, and health indicators, with the goal of optimizing growth performance while minimizing feed costs and environmental footprint. The specific objectives are to (i) develop an integrated precision-feeding protocol that responds to real-time sensor data to modulate concentrate and milk replacer provisioning, (ii) implement a sensor-enabled feeding apparatus on a commercial dairy farm and document operational feasibility, (iii) evaluate growth trajectories, feed efficiency, starter intake, health events, and weaning outcomes relative to conventional feeding, and (iv) conduct a cost-benefit and environmental impact analysis to determine scalability. The study tests the hypothesis that precision-feeding calves with sensor-guided rations improves average daily gain (ADG) and feed conversion ratio (FCR) without increasing medical interventions, compared with standard ad-libitum feeding. A mixed-methods approach guides the inquiry. The experimental component uses a controlled, longitudinal design with 120 Holstein-Friesian calves enrolled at birth and assigned to treatment (n=60) and control (n=60) groups, matched for birth weight, dam parity, and ambient conditions. The intervention employs an automated feeding station equipped with load-cells for intake measurement, RFID-based animal identification, near-infrared spectroscopy for body condition proxies, and environmental sensors (temperature-humidity index). The treatment group receives dynamically adjusted volumes of milk replacer and starter concentrate determined by a rule-based algorithm integrating past intake, growth rate, and health indicators (e.g., fecal consistency, nasal temperature). Data collection instruments include electronic milk-feed logs, scale-measured weekly body weight, daily health check sheets, and monthly blood snapshots (total protein, glucose, cortisol as welfare indicators). Instrument validity is ensured through calibration procedures, pilot testing, and cross-validation with manual measurements. Data analysis adopts regression analyses to quantify growth and intake relationships, mixed-effects models to account for repeated measures and farm-level clustering, and ANOVA to compare treatment and control outcomes. A cost-benefit analysis applies net present value and return on investment (ROI) metrics, while life-cycle assessment estimates the environmental footprint via feed efficiency and methane emission proxies. Theoretical framing draws on the Theory of Planned Behavior to interpret producer adoption intentions and the Optimal Foraging Theory to rationalize nutrient allocation under constraint. Expected findings include improved ADG by 8–12% and reduced FCR by 6–9% in the precision-fed group, with lower incidence of digestive disturbances and comparable or reduced antimicrobial usage. Sensor-driven adjustments are anticipated to yield smoother growth curves and more consistent weaning weights. The study is also expected to reveal favorable economic outcomes, with payback periods within two lactations and a measurable reduction in feed waste (?15%). Environmental analysis should demonstrate reduced methane emission intensity per kilogram of gain due to better conversion efficiency. The research will contribute to knowledge by providing empirical evidence on the feasibility, effectiveness, and economic viability of sensor-enabled precision feeding in dairy calves, clarifying optimum data-driven decision rules and identifying practical barriers to on-farm adoption. The main conclusion is that automated, sensor-guided precision feeding can enhance calf performance and welfare while delivering tangible economic and environmental benefits under real-world management, provided that robust data infrastructure, farmer training, and maintenance protocols are established. Recommendations include scaling to multi-site trials, refining the decision algorithm with machine learning components, integrating automated health monitoring, and developing policy guidelines for broader uptake in the dairy sector.