Prediction of bite number and herbage intake by an accelerometer-based system in dairy sheep exposed to different forages during short-term grazing tests

Giovanetti, V.; Cossu, R.; Molle, G.; Acciaro, M.; Mameli, M.; Cabiddu, A.; Serra, Moisès Serra i; Manca, C.; Rassu, S.P.G.; Decandia, M.; Dimauro, Corrado

doi:10.1016/j.compag.2020.105582

Cited by 15 publications

(6 citation statements)

References 23 publications

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“…F1-score 1.00 1.00 0.97 0.99 0.99 1 Ing-Che represents the behavior of ingesting-chewing. 2 WM represents the harmonic mean of the four behavior categories.…”

Section: Prediction Of Feed Intake Ratementioning

confidence: 99%

“…Changes in feeding behavior and feed intake are important indicators to help identifying the health and wellbeing of ruminants, and to understand the interaction between ruminants and their physiological state [1,2]. The reduced feeding time of dairy or beef cattle indicates intake deficiencies, and, in such cases, supplementary feeding is necessary.…”

Section: Introductionmentioning

confidence: 99%

“…However, these devices can only measure primary behaviors such as feeding, lying, and walking, rather than types of activity including of jaw movements during feeding, such as ingesting (collecting feed into mouth), chewing, etc. The rhythmicity and timeline of jaw movement can be used to deduce the occurrence, duration and daily variation of feeding behaviors and, consequently, estimate cattle's feed intake [2,5]. Therefore, various sensors have been developed using the rhythmic movement of the jaw to monitor the feeding behavior of ruminants in the past few years.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that feeding behavior measurements had potential for estimating feed intake, but the model revealed a low R 2 , which is not sufficient for quantitative estimations. Giovanetti [2] applied partial least square regression analysis (PLSR) to acceleration variables to predict the behavioral traits and herbage intake of sheep during short-term grazing. It showed a moderate precision (R 2 = 0.71) to predict herbage intake, and the authors suggested the external validation of the derived models for better estimates in the future.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer

et al. 2022

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The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R2, RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake.

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“…F1-score 1.00 1.00 0.97 0.99 0.99 1 Ing-Che represents the behavior of ingesting-chewing. 2 WM represents the harmonic mean of the four behavior categories.…”

Section: Prediction Of Feed Intake Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer

et al. 2022

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“…Similarly, a range of sensors with the potential to predict individual cow intakes are currently being developed, and these, if combined with accurate composition of rations offered and milk N content, may allow NUE to be estimated. These include neck-mounted accelerometers, which have been used to determine grazing time and speed of bite, and whilst there are promising developments for the use of these sensors to estimate DMI of grazing ruminants [ 88 , 89 ], further work is needed to improve accuracy due to varying quality and compositions of pasture. Nevertheless, these approaches do require the N content of all diet components to be recorded accurately.…”

Section: Strategies For Estimating the Efficiency Of Nitrogen Utilmentioning

confidence: 99%

Proxy Measures and Novel Strategies for Estimating Nitrogen Utilisation Efficiency in Dairy Cattle

Lavery

Ferris

2021

Animals

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The efficiency with which dairy cows convert dietary nitrogen (N) to milk N is generally low (typically 25%). As a result, much of the N consumed is excreted in manure, from which N can be lost to the environment. Therefore there is increasing pressure to reduce N excretion and improve N use efficiency (NUE) on dairy farms. However, assessing N excretion and NUE on farms is difficult, thus the need to develop proximate measures that can provide accurate estimates of nitrogen utilisation. This review examines a number of these proximate measures. While a strong relationship exists between blood urea N and urinary N excretion, blood sampling is an invasive technique unsuitable for regular herd monitoring. Milk urea N (MUN) can be measured non-invasively, and while strong relationships exist between dietary crude protein and MUN, and MUN and urinary N excretion, the technique has limitations. Direct prediction of NUE using mid-infrared analysis of milk has real potential, while techniques such as near-infrared spectroscopy analysis of faeces and manure have received little attention. Similarly, techniques such as nitrogen isotope analysis, nuclear magnetic resonance spectroscopy of urine, and breath ammonia analysis may all offer potential in the future, but much research is still required.

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A 4$$\mu$$W Low-Power Audio Processor System for Real-Time Jaw Movements Recognition in Grazing Cattle

Martinez-Rau

Weißbrich

Payá-Vayá

2022

J Sign Process Syst

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Prediction of bite number and herbage intake by an accelerometer-based system in dairy sheep exposed to different forages during short-term grazing tests

Cited by 15 publications

References 23 publications

Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer

Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer

Proxy Measures and Novel Strategies for Estimating Nitrogen Utilisation Efficiency in Dairy Cattle

A 4$$\mu$$W Low-Power Audio Processor System for Real-Time Jaw Movements Recognition in Grazing Cattle

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