Comparative evaluation of the behaviour of Holstein-Friesian and Jersey cows under European pasture-based management systems

Summary in the original language of the document

Grazing is an important aspect of the organic dairy production system and is considered the most effective means of reducing costs of milk production when managed properly. Grazing requires a well-adapted dairy cattle breed with high efficiency in converting energy intake to milk production. In organic dairy farming, it is essential to observe the behavioural and physiological changes of cows as early as possible to ensure better health, welfare, and productivity. The objective of the current study was to investigate whether the differences in grazing behaviour exist between Jersey (Jer) and Holstein Friesian (HF) cows under pasture-based management systems. The behavioural activities of 62 lactating dairy cows (36 HF and 26 Jersey cows) at Zegveld research unit, was recorded by the ear tag microchip, sensOor (AGIS, Automatisering) and the SmartTag neck sensor (NEDAP, The Netherlands) from April to October 2016. The activities recorded by sensOor included eating/grazing
and rumination while the NEDAP recorded eating/grazing and standing indoors and on pastures, respectively. Furthermore, the current study included two grazing systems kurzrasenweide (KR) and strip grazing (SG) and two levels of degradable proteins (OEB+ and OEB-). The differences in dietary level were created by supplementing the cows with 6 kg of concentrates per day with either an OEB+/- value of -50 or +50 g/kg DM according to DVE/OEB2007 protein evaluation system for dairy cows. The hypothesis tested was Jersey cows perform better under grazing conditions as compared to the HF cows and also, supplementation with low degradable protein might have increased the time spent in grazing as compared to high degradable protein. The behavioural data and performance evaluation were coded and analysed by Genstat 18th edition and Statistical Package for Social Sciences version 23, respectively.
The mean BW, FPCM and daily NEL, required was significantly lower for Jersey cows as compared to HF in all experimental groups, throughout the experimental period (p-value ≤0.05). The NEDAP output shows significant differences in the time spent eating/grazing and standing indoors and on pastures. However, with an exception for rumination activities, the current version of sensOor was overestimating and unable to differentiate between eating concentrate/roughages indoors and grazing on pastures. Based on the sensOor output HF cows spent longer time in rumination as compared to Jersey cows throughout the experimental period. NEDAP output shows that Jersey cows spent longer time in standing, associated with their smaller body size and less energy requirements. Also, Jersey cows spent longer time eating/grazing as compared to HF, both indoor and outdoor, associated with higher intake capacity per kg body weight. Unlike Jersey cows, the time spent grazing by the HF varied between the grazing systems. When cows were grazing in June and August, HF spent a short time grazing under SG as compared to KR system, which is associated with the quality of pastures.
On the other hand, when cows were on pastures Jersey cows were not affected by changing the protein level in the diet, which influenced their consistency in percentage time spent on grazing in both KR and SG systems. However, HF were affected by reducing the protein level in diet, associated with their higher energy requirements for maintenance and production. The persistence of Jersey cows under low protein levels and the higher efficiency in grazing provides an opportunity for reducing the costs of production and feed-food competition for cereals, required for human and monogastric animals. The use of sensor technology could be an early warning tool in monitoring the behavioural activities of cows. However, further improvement of the sensOor is required to avoid overestimation of behaviour activities and to differentiate between eating and grazing. The use of sensor technology might be useful to both farmers and researchers in reducing the time spent on monitoring behavioural activities of individual cows.