A machine learning approach to simulate cattle growth at pasture using remotely collected walk-over weights

IF 6.1 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Agricultural Systems Pub Date : 2025-03-28 DOI:10.1016/j.agsy.2025.104332

Tek Raj Awasthi , Ahsan Morshed , Dave L. Swain

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引用次数: 0

Abstract

Context

The growing interest in the use and implementation of remote and automated technologies, such as walk-over (WO) weighing, has made the availability of a large volume of data. However, animal behaviour, sensitivity and repeatability of WO weighing scales and animal's physiological state could impact the accuracy of recorded weights. Therefore, WO weight data needs through processing before it can be utilized effectively for developing livestock management tools.

Objective

This study is designed to implement a machine learning (ML) approach to develop a simulation model capable of predicting cattle growth using remotely collected WO weight data from pasture-based beef production.

Methods

WO weight data collected from two beef production farms (Belmont and Tremere) of central Queensland, Australia has been used to train an XGBoost ML model which is then implemented in an algorithm that takes the number of male and females of Belmont Red, Brahman, Composite and unknown breed of animals, number of days to simulate, birth date range of animals and weather conditions at the farm to simulate as input parameters and predicted daily weights as the output.

Results and conclusions

The simulation model obtained Lin's concordance coefficient (CCC), root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R²) of 0.84, 32.60 kg, 15.23 % and 0.77 respectively for test set 1 and CCC, RMSE, MAPE and R² of 0.82, 38.30 kg, 15.20 %, and 0.73 respectively for test set 2, when compared with the actual WO weights. The mean difference between the simulated and observed weights is found to be −1.2 kg (SD 27.3 kg), where the 95 % agreement limit (at 1.96 SD) is −54.8 kg to 52.3 kg.

Significance

The results suggest that autonomously collected WO weights can be an important data source for developing strategies for livestock management, and the simulation model developed in this study can be used by cattle producers to predict the growth patterns of their herd and by researchers to generate cattle growth data to implement in the development of livestock managements tools.

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来源期刊

Agricultural Systems 农林科学-农业综合

CiteScore

13.30

自引率

7.60%

发文量

174

审稿时长

30 days

期刊介绍： Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments. The scope includes the development and application of systems analysis methodologies in the following areas: Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making; The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment; Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems; Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.