The aim of this study was to identify a set of feeding behaviour and activity related variables that could potentially detect a shortage of available feed for the individual cow on pasture. A group of lactating cows was offered 100% of their intake capacity as herbage allowance throughout a 10-week experimental period, while another group was offered 60% of their intake allowance, either for a two week or six week period in springtime. Each cow was equipped with an automated noseband sensor. The data were analysed by using a binomial generalized lineal model (GLM). The GLM was examined for the classification of full or restricted herbage allowance as a function of a previously identified set of characteristics. The model was further refined by including additional characteristics, which achieved higher prediction performance. For the combined data, the refined model achieved 77% accuracy, 75% sensitivity, 78% specificity and F-score 0.76 towards a decision support system for grass utilisation in pasture based milk production.

Modelling restricted feeding conditions on cows’ feeding behaviour on pasture-based milk production systems to develop a Decision Support System / A. Shafiullah, J. Werner, E. Kennedy, L. Leso, B. O’ Brien, C. Umstätter. - ELETTRONICO. - (2019), pp. 152-158. (Intervento presentato al convegno Precision Livestock Farming ’19).

Modelling restricted feeding conditions on cows’ feeding behaviour on pasture-based milk production systems to develop a Decision Support System

L. Leso
Investigation
;
2019

Abstract

The aim of this study was to identify a set of feeding behaviour and activity related variables that could potentially detect a shortage of available feed for the individual cow on pasture. A group of lactating cows was offered 100% of their intake capacity as herbage allowance throughout a 10-week experimental period, while another group was offered 60% of their intake allowance, either for a two week or six week period in springtime. Each cow was equipped with an automated noseband sensor. The data were analysed by using a binomial generalized lineal model (GLM). The GLM was examined for the classification of full or restricted herbage allowance as a function of a previously identified set of characteristics. The model was further refined by including additional characteristics, which achieved higher prediction performance. For the combined data, the refined model achieved 77% accuracy, 75% sensitivity, 78% specificity and F-score 0.76 towards a decision support system for grass utilisation in pasture based milk production.
2019
Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019
Precision Livestock Farming ’19
Goal 2: Zero hunger
Goal 4: Quality education
Goal 9: Industry, Innovation, and Infrastructure
A. Shafiullah, J. Werner, E. Kennedy, L. Leso, B. O’ Brien, C. Umstätter
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1196071
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