Time-budget and location of activities in the paddock can be estimated from GPS-data. 10. International Symposium on the Nutrition of Herbivores (ISNH

Aubin, Sébastien

Bedere, Nicolas

Chauvin, Alain

Couvreur, Sebastien

goumand, E.

Madouasse, Aurélien

Magnier, Jacques

Plantier, Guy

Riaboff, Lucile

English

Archive Ouverte en Sciences de l'Information et de la Communication

425 Session 3: Precision herbivore nutrition 
Time-budget and location of activities in the paddock can be estimated from GPS-data 
Lucile Riaboff1,3, Nicolas Bédère2, Sébastien Couvreur2, Sébastien Aubin1, Etienne Goumand3, Aurélien Madouasse4, Jacques 
Magnier2, Alain Chauvin4, Guy Plantier1 
1ESEOTech – LAUM, Angers, France, 2URSE, Ecole Supérieure d’Agricultures, Univ. Bretagne Loire, Angers, France, 3Terrena 
Innovation, Ancenis, France, 4BIOEPAR, INRA, Oniris, Nantes, France 
E-mail: lucile.riaboff@eseo.fr 
 
Take home message Cow time-budget and organization of activities in the paddock can be studied efficiently without laborious 
observation using GPS data and GPS-located resources. 
Introduction Emerging tools like embedded GPS sensors could help to optimize pasture management (Manning et al., 2017), with 
a better understanding of the use of the paddocks by the cows. This paper aims to study (1) whether embedded GPS sensors could 
predict the behaviour of dairy cows and (2) enable to study the organisation of their activities during the day and its association 
with pasture characteristics or other elements in the paddock. 
Materials & methods Fourteen Hosltein cows were tracked with GPS sensors mounted on collars in a commercial dairy farm 
(Lion d’Angers, France). They grazed in temporary pasture, sown with a mixture of perennial ryegrass and white clover and were 
milked every day at 9 am and 6 pm. The distribution of cows on the pasture was studied for a whole day by computing the percentage 
of cows located in each 3*3 m area. In order to discriminate between resting, grazing and walking activities, behaviours were 
recorded by two observers for each cow successively, during the entire day. Distances and turning angles between each fixe were 
computed in order to calibrate a decision tree (rpart package, R). Behaviours were then predicted using the calibrated decision tree. 
A chi square test was performed in R to compare the time-budget per half day. The location of main activities in the field was also 
studied. To explore the potential link between location of predicted behaviours and resources distribution, samples of vegetation 
cover were collected and GPS-located. The percentage of dry matter was obtained and the ratio of perennial ryegrass, white clover 
and self-propagating plants were determined, other species were regarded as negligible. A multinomial analysis was performed 
with nnet and car packages in R to conclude on the relationship with behaviours. 
Results The distribution of the cows in the paddock area was heterogeneous, with some areas avoided and others frequently visited 
(Figure 1a). A satisfactory calibration of the decision tree was obtained with a misclassification rate of the behaviours of 10%, as 
expected (de Weerd et al., 2015). The time-budget was significantly different between each half day (p < 0.0001), with 50.4±6.2% 
of grazing (ranging from 40.0 to 58.8% among cows) and 49.6±6.2% of resting (ranging from 41.2 to 60.0%) in the morning versus 
38.5±9.3% (ranging from 27.1 to 59.2%) and 61.5±9.4% (ranging from 40.3 to 72.9%) in the afternoon respectively. Cows seemed 
to organize their activities inside the paddock (e.g. more resting close to the drinking trough; Figure 1b). No significant relationship 
between behaviour and DM and floral distributions was observed. 
 
 
 
 
 
 
 
 
 
Conclusion The use of GPS embedded sensors highlighted a heterogeneous distribution of cows in the paddock, with some areas 
being preferentially used for resting or grazing. In this study, this organization of activities seemed to be influenced by structural 
elements in the paddock, like the drinking trough, rather than pasture characteristics. 
Acknowledgements This study was funded by the region Pays-de-la-Loire within the framework of the Vagabond project. 
References 
Manning J, Greg C, Luciano G, Evelyn H, Andrew M and Lachlan I 2017. Agriculture 7, 45. 
de Weerd N, van Langevelde F, van Oeveren H, Nolet BA, Kölzsch A, Prins HHT and de Boer WF 2015. Plos one 10, 1-17 
  
Figure 1 a) Distribution of cows (%) inside the paddock. The more 
the area was frequented the darker the square is. b) Dominant 
behaviour in the same 3*3m area predicted with a decision tree. 
 


Time-budget and location of activities in the paddock can be estimated from GPS-data

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Time-budget and location of activities in the paddock can be estimated from GPS-data

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