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Research on the Assessment of Dairy Cow Dry Matter Intake Using ITSO-Optimized Stacking Ensemble Learning.

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Title: Research on the Assessment of Dairy Cow Dry Matter Intake Using ITSO-Optimized Stacking Ensemble Learning.
Authors: Wang, Shuairan, Long, Ting, Wei, Xiaoli, Guo, Qinzu, Guo, Hongrui, Shen, Weizheng, Gu, Zhixin
Source: Animals (2076-2615); Feb2026, Vol. 16 Issue 4, p625, 25p
Subject Terms: ENSEMBLE learning, OPTIMIZATION algorithms, FEED utilization efficiency, DAIRY farming, CATTLE nutrition, MACHINE learning, ANIMAL feeding
Abstract: Simple Summary: Dry matter intake (DMI) is a key indicator of the nutritional status and feeding efficiency of dairy cows and is essential for precision feeding management. However, direct measurement of DMI is labor-intensive, costly, and difficult to implement in commercial dairy farms. In this study, we developed an intelligent assessment method for cow DMI by combining behavioral and physiological data with an optimized ensemble learning approach. Using cow body weight, lying duration, lying times, rumination time, foraging duration, walking activity, and the concentrate-to-roughage ratio as inputs, an improved Tuna Swarm Optimization-based Stacking model was established to accurately assess DMI. The proposed method achieved high prediction accuracy and outperformed several commonly used machine learning models. This approach provides a practical, low-cost solution for real-time DMI assessment, helping farmers optimize feeding strategies, improve production efficiency, and support sustainable dairy farming. Dry matter intake (DMI) in dairy cows is a critical indicator of nutrient intake from feed, serving as the cornerstone of precision feeding practices, playing a critical role in improving production efficiency and enhancing the quality of dairy products. To address the high costs of traditional measurement methods and the structural complexity and large parameter counts of neural network models, this study proposes a Stacking ensemble learning model to assess DMI, with model parameters optimized using the Tuna Swarm Optimization (TSO) algorithm to enhance assessment accuracy, taking cow body weight, lying duration, lying times, rumination duration, foraging duration, walking steps, and the concentrate-to-roughage feed ratio as input variables. To further improve TSO's search efficiency and spatial exploration, this study introduces Sine–Logistic chaotic mapping, Levy flight, and Gaussian random walk strategy to optimize the TSO algorithm, developing the improved Tuna Swarm Optimization (ITSO). ITSO-optimized Stacking model achieved superior performance in DMI assessment, with an accuracy of 95.84%, significantly outperforming SVR, RF, DT, GBR, ETR, and AdaBoost models. This study provides a robust tool for precision feeding, contributing to optimizing cow feeding strategies, improving farm efficiency, and supporting sustainable dairy farming practices. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract:Simple Summary: Dry matter intake (DMI) is a key indicator of the nutritional status and feeding efficiency of dairy cows and is essential for precision feeding management. However, direct measurement of DMI is labor-intensive, costly, and difficult to implement in commercial dairy farms. In this study, we developed an intelligent assessment method for cow DMI by combining behavioral and physiological data with an optimized ensemble learning approach. Using cow body weight, lying duration, lying times, rumination time, foraging duration, walking activity, and the concentrate-to-roughage ratio as inputs, an improved Tuna Swarm Optimization-based Stacking model was established to accurately assess DMI. The proposed method achieved high prediction accuracy and outperformed several commonly used machine learning models. This approach provides a practical, low-cost solution for real-time DMI assessment, helping farmers optimize feeding strategies, improve production efficiency, and support sustainable dairy farming. Dry matter intake (DMI) in dairy cows is a critical indicator of nutrient intake from feed, serving as the cornerstone of precision feeding practices, playing a critical role in improving production efficiency and enhancing the quality of dairy products. To address the high costs of traditional measurement methods and the structural complexity and large parameter counts of neural network models, this study proposes a Stacking ensemble learning model to assess DMI, with model parameters optimized using the Tuna Swarm Optimization (TSO) algorithm to enhance assessment accuracy, taking cow body weight, lying duration, lying times, rumination duration, foraging duration, walking steps, and the concentrate-to-roughage feed ratio as input variables. To further improve TSO's search efficiency and spatial exploration, this study introduces Sine–Logistic chaotic mapping, Levy flight, and Gaussian random walk strategy to optimize the TSO algorithm, developing the improved Tuna Swarm Optimization (ITSO). ITSO-optimized Stacking model achieved superior performance in DMI assessment, with an accuracy of 95.84%, significantly outperforming SVR, RF, DT, GBR, ETR, and AdaBoost models. This study provides a robust tool for precision feeding, contributing to optimizing cow feeding strategies, improving farm efficiency, and supporting sustainable dairy farming practices. [ABSTRACT FROM AUTHOR]
ISSN:20762615
DOI:10.3390/ani16040625