Work

A decision support framework for reliable early prediction of long-term cumulative milk yield in dairy cows

Year: 2026

Type: article

Abstract: OBJECTIVE: Early prediction of cumulative milk yield from initial lactation records enables timely culling, selective breeding, and optimized feed allocation, thereby improving herd efficiency and farm profitability. However, predicting long-term production remains challenging because of high variability in early-lactation patterns and limited available data. This study aimed to develop and compare predictive models for estimating long-term cumulative milk yield from daily production records and to identify the optimal approach and minimum days in milk (DIM) threshold for reliable prediction. METHODS: We used 211 lactation datasets with complete records up to 305 DIM, selected from daily milk yield records of 727 dairy cows. Six models were evaluated: three statistical methods (simple averaging, adjustment factor, and correction coefficient) and three ensemble machine learning algorithms (CatBoost, XGBoost, and Random Forest). Because lactation patterns differ between primiparous and multiparous cows, parity-specific models were also developed. Predictive performance was assessed using mean absolute percentage error (MAPE) between predicted and actual yields. RESULTS: The 305-day cumulative milk yield was predicted within an 11% error margin across all base DIMs, with the smallest discrepancy of 0.5 kg observed in the XGBoost model. Machine learning models showed better performance during early lactation (MAPE 8.878 at base DIM 30 for parity ≥2), whereas statistical models achieved higher accuracy after base DIM 120 (MAPE 4.311 at base DIM 150 for parity 1). CONCLUSION: This study provides a lactation stage-dependent and parity-specific prediction framework that bridges early-lactation uncertainty and long-term productivity estimation. By enabling model selection according to data availability and lactation stage, the proposed approach supports the practical application of predictive analytics in dairy farming and provides a decision-support framework for early culling, feeding optimization, and productivity-based herd management.

Source: Animal Bioscience

DOI: 10.5713/ab.260424

Authors: Jae-Woo Song, Dong-Hyeon Kim, Seung-Hyun Lee, Seongwon Seo, Wang-Hee Lee

Institution: Chungnam National University

Language: English

Topic: Genetic and phenotypic traits in livestock

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $700