Honigeinlagerung

Der Wassergehalt des von den Pflanzen produzierten Nektars kann variieren. Um seine Konservierung in den Waben zu gewährleisten, darf er nicht gären. Deshalb wird er von den Bienen in Honig umgewandelt, indem sie seinen Wassergehalt senken und folglich den Zuckergehalt erhöhen. Dies verhindert die Entwicklung der Mikroorganismen. In der Dunkelheit des Bienenstocks ist es schwierig, den Herstellungsprozess des Honigs zu beobachten. Aus diesem Grund ist er auch nur wenig erforscht. Um die Umwandlung von Nektar in Honig zu untersuchen, haben wir die Technik der Tomographie eingesetzt. Sie ermöglicht es, den Zuckergehalt des eingelagerten Nektars sehr präzise zu messen, ohne den Bienenstock öffnen zu müssen und das Bienenvolk zu stören.

Evaluation of a binary classification approach to detect herbage scarcity based on behavioral responses of grazing dairy cows.

Institutional Repository Agroscope

Hart L., Dickhoefer U., Paulenz E., Umstaetter C.

Evaluation of a binary classification approach to detect herbage scarcity based on behavioral responses of grazing dairy cows.

Sensors, 22, 2022, 968-995.

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Abstract:

In precision grazing, pasture allocation decisions are made continuously to ensure demandbased feed allowance and efficient grassland utilization. The aim of this study was to evaluate existing prediction models that determine feed scarcity based on changes in dairy cow behavior. During a practice-oriented experiment, two groups of 10 cows each grazed separate paddocks in half-days in six six-day grazing cycles. The allocated grazing areas provided 20% less feed than the total dry matter requirement of the animals for each entire grazing cycle. All cows were equipped with noseband sensors and pedometers to record their head, jaw, and leg activity. Eight behavioral variables were used to classify herbage sufficiency or scarcity using a generalized linear model and a random forest model. Both predictions were compared to two individual-animal and day-specific reference indicators for feed scarcity: reduced milk yields and rumen fill scores that undercut normal variation. The predictive performance of the models was low. The two behavioral variables “daily rumination chews” and “bite frequency” were confirmed as suitable predictors, the latter being particularly sensitive when new feed allocation is present in the grazing set-up within 24 h. Important aspects were identified to be considered if the modeling approach is to be followed up.

Digital Object Identifier (DOI): https://doi.org/10.3390/s22030968
Publication-ID (Web Code): 48783 Sending by e-mail

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Honigeinlagerung

Evaluation of a binary classification approach to detect herbage scarcity based on behavioral responses of grazing dairy cows.