Literature DB >> 33946608

Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions.

Marco Bovo1, Miki Agrusti1, Stefano Benni1, Daniele Torreggiani1, Patrizia Tassinari1.   

Abstract

Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016-2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production.

Entities:  

Keywords:  heat stress; livestock sustainability; machine learning; precision livestock farming; random forest

Year:  2021        PMID: 33946608     DOI: 10.3390/ani11051305

Source DB:  PubMed          Journal:  Animals (Basel)        ISSN: 2076-2615            Impact factor:   2.752


  12 in total

1.  A generalised additive model to characterise dairy cows' responses to heat stress.

Authors:  S Benni; M Pastell; F Bonora; P Tassinari; D Torreggiani
Journal:  Animal       Date:  2019-07-31       Impact factor: 3.240

2.  Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees.

Authors:  Dariusz Piwczyński; Beata Sitkowska; Magdalena Kolenda; Marcin Brzozowski; Joanna Aerts; Pamela M Schork
Journal:  Anim Sci J       Date:  2020 Jan-Dec       Impact factor: 1.749

3.  Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production.

Authors:  Ilan Halachmi; Marcella Guarino; Jeffrey Bewley; Matti Pastell
Journal:  Annu Rev Anim Biosci       Date:  2018-11-28       Impact factor: 8.923

4.  Precision livestock farming technologies for welfare management in intensive livestock systems.

Authors:  D Berckmans
Journal:  Rev Sci Tech       Date:  2014-04       Impact factor: 1.181

Review 5.  Review: Milking robot utilization, a successful precision livestock farming evolution.

Authors:  A J John; C E F Clark; M J Freeman; K L Kerrisk; S C Garcia; I Halachmi
Journal:  Animal       Date:  2016-04-07       Impact factor: 3.240

6.  Cow individual activity response to the accumulation of heat load duration.

Authors:  Julia Heinicke; Stephanie Ibscher; Vitaly Belik; Thomas Amon
Journal:  J Therm Biol       Date:  2019-03-22       Impact factor: 2.902

7.  A Survey of Dairy Cattle Behavior in Different Barns in Northern Italy.

Authors:  Daniela Lovarelli; Alberto Finzi; Gabriele Mattachini; Elisabetta Riva
Journal:  Animals (Basel)       Date:  2020-04-19       Impact factor: 2.752

8.  Effects of Climatic Conditions on the Lying Behavior of a Group of Primiparous Dairy Cows.

Authors:  Emanuela Tullo; Gabriele Mattachini; Elisabetta Riva; Alberto Finzi; Giorgio Provolo; Marcella Guarino
Journal:  Animals (Basel)       Date:  2019-10-26       Impact factor: 2.752

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  3 in total

Review 1.  Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions.

Authors:  Sarah Morrone; Corrado Dimauro; Filippo Gambella; Maria Grazia Cappai
Journal:  Sensors (Basel)       Date:  2022-06-07       Impact factor: 3.847

Review 2.  Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study.

Authors:  Philip Shine; Michael D Murphy
Journal:  Sensors (Basel)       Date:  2021-12-22       Impact factor: 3.576

Review 3.  Predictive Models of Dairy Cow Thermal State: A Review from a Technological Perspective.

Authors:  Soraia F Neves; Mónica C F Silva; João M Miranda; George Stilwell; Paulo P Cortez
Journal:  Vet Sci       Date:  2022-08-08
  3 in total

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