A data-driven approach for optimising supplement allocation to individual lactating dairy cows in pasture-based systems
Year: 2025
Type: article
Abstract: With feed costs accounting for about 40-60% of milk production expenses in Australia, efficient supplementary concentrate allocation is crucial for profitability. Despite an increase in concentrate use per cow over the past decade, the average milk yield response remains about 1L per kg of dry matter concentrate. While data-driven optimisation has proven valuable in other sectors, its potential in dairy farming is underexplored. This study aims to develop a machine learning-based method to optimise individual cow supplement allocation, using similar total daily concentrate with a tolerance range allowing for a 2-10% decrease, to maximise milk yield. Data from a controlled field study involving 130 lactating Holstein-Friesians (32,504 records) were analysed.Sixteen machine learning algorithms were evaluated to predict milk yield based on concentrate allocation and available cattle data (days in milk, daily milk yield and liveweight, and parity number). The Random Forest (RF) model was the best performer, achieving an R² of 0.60 and RMSE of 4.20 L/cow/day. Then 7,371 records from 81 cows over 91 days were employed to run the concentrate levels optimisation using the Dirichlet-Rescale (DRS) algorithm and Monte Carlo simulation. The RF model and SciPy optimisation determined optimal individual cow allocations (5-9 kg/cow.day-1). Implementing this resulted in a herd-level 8% increase in daily milk yield. This study highlights potential benefits of adopting data-driven algorithms for individualised dairy feed optimisation, which not only suggests an improvement over conventional flat-rate methods but opens avenues for future research aimed at validating the practical efficacy and economic impact of these advanced approaches in actual dairy farming contexts.
Source: Smart Agricultural Technology
Authors: Blessing Nnenna Azubuike, Anna Chlingaryan, Martin Correa-Luna, Cameron E.F. Clark, Sergio C. Garcia
Institution: The University of Sydney
Language: English
Subfield: Agronomy and Crop Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,770
Funder: University of Sydney
OpenAlex ID: W4416712958