The Behavioural Responses of Beef Cattle (Bos taurus) to Declining Pasture Availability and the Use of GNSS Technology to Determine Grazing Preference

Manning, Jaime; Cronin, G. M.; González, L. A.; Hall, Evelyn; Merchant, Andrew; Ingram, Lachlan J.

doi:10.3390/agriculture7050045

Cited by 31 publications

(18 citation statements)

References 33 publications

(38 reference statements)

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“…The use of the food resources and the occupation of grazing space can be very heterogeneous in such conditions due to the ruminants’ grazing behaviour [ 6 ]. A better understanding of animal behaviour and environmental interactions are required to optimise the management of livestock and the spaces in which they graze [ 23 ]. Global navigation satellite system (GNSS) position loggers could determine livestock grazing preference and hence improve pasture management and paddock utilisation [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…A better understanding of animal behaviour and environmental interactions are required to optimise the management of livestock and the spaces in which they graze [ 23 ]. Global navigation satellite system (GNSS) position loggers could determine livestock grazing preference and hence improve pasture management and paddock utilisation [ 23 ]. Combining technologies for monitoring spatial behaviour of livestock with technologies that monitor soil and pasture availability, offers potential solutions to emerging issues and the opportunity to improve the management and welfare of extensive animal production [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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A Holistic Approach to the Evaluation of the Montado Ecosystem Using Proximal Sensors

Serrano

Shahidian

Silva

et al. 2018

Sensors

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Abstract:The Montado is a silvo-pastoral system characterized by open canopy woodlands with natural or cultivated grassland in the undercover and grazing animals. The aims of this study were to present several proximal sensors with potential to monitor relevant variables in the complex montado ecosystem and demonstrate their application in a case study designed to evaluate the effect of trees on the pasture. This work uses data collected between March and June 2016, at peak of dryland pasture production under typical Mediterranean conditions, in twenty four sampling points, half under tree canopy (UTC) and half outside tree canopy (OTC). Correlations were established between pasture biomass and capacitance measured by a commercial probe and between pasture quality and normalized difference vegetation index (NDVI) measured by a commercial active optical sensor. The interest of altimetric and apparent soil electrical conductivity maps as the first step in the implementation of precision agriculture projects was demonstrated. The use of proximal sensors to monitor soil moisture content, pasture photosynthetically active radiation and temperature helped to explain the influence of trees on pasture productivity and quality. The significant and strong correlations obtained between capacitance and pasture biomass and between NDVI and pasture nutritive value (in terms of crude protein, CP and neutral detergent fibre, NDF) can make an important contribution to determination of key components of pasture productivity and quality and implementation of site-specific pasture management. Animal tracking demonstrated its potential to be an important tool for understanding the interaction between various factors and components that interrelate in the montado ecosystem and to support grazing management decisions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Holistic Approach to the Evaluation of the Montado Ecosystem Using Proximal Sensors

Serrano

Shahidian

Silva

et al. 2018

Sensors

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“…We were not able to explore such changes in our study because no challenging situations were recorded during the 5 days of experimentation (thermoneutrality, liberal grazing system, etc. ), but our methodological framework can be used to explore the changes in both feeding behaviors [ 44 ]—static [ 45 ] and dynamic [ 46 ]—under different stressful situations. Finally, time-budgets were computed in our study but other indicators can be calculated from the predicted behaviors, such as the number of bouts and their average duration.…”

Section: Discussionmentioning

confidence: 99%

Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics

Riaboff

Couvreur

Madouasse

et al. 2020

Sensors

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Our aim in this study was to investigate whether the behaviors of dairy cows on pasture, predicted with accelerometer data and combined with GPS data, can be used to better understand the relationship between behaviors and pasture characteristics. During spring 2018, 26 Holstein cows were equipped with a 3D-accelerometer and a GPS sensor fixed on a neck-collar for five days. The cows grazed alternatively in permanent and in temporary grasslands. The structural elements, soil moisture, slope and botanical characteristics were identified. Behaviors were predicted every 10 s from the accelerometer data and combined with the GPS data. The time-budgets expressed in each characterized zone of 8 m × 8 m were calculated. The relation between the time-budgets and pasture characteristics was explored with a linear mixed model. In the permanent grassland, dairy cows spent more time under a tree to ruminate (p < 0.001) and to rest (p < 0.001) and more time to graze in areas with Holcus lanatus (p < 0.001). In the temporary grassland, behavior was influenced by the external environment (presence of other animals on the farm; p < 0.05). Thus, this methodology seems relevant to better understand the relationship between the behaviors of dairy cows and grazing conditions to develop precision grazing.

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“…Many of the variables that influence requirements can be measured in near real-time using sensor and information technologies, and be utilised for precision nutritional management, such as diet formulation or controlling feed delivery or access to particular feeds and amounts. However, precision nutrition of animals may also involve managing particular processes in the flow of nutrients such as designing grazing systems to optimise energy expenditure, grazing management and pasture utilisation rate (González et al, 2014a;Manning et al, 2017); or facilitate the breeding of animals that are more efficient for certain nutritional scenarios such as prolonged dry seasons; or optimise slaughter strategies according to cost and value of weight gain.…”

Section: Introductionmentioning

confidence: 99%

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

Gonzalez

Kyriazakis

Tedeschi

2018

Animal

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A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data ‘as is’, further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

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The Behavioural Responses of Beef Cattle (Bos taurus) to Declining Pasture Availability and the Use of GNSS Technology to Determine Grazing Preference

Cited by 31 publications

References 33 publications

A Holistic Approach to the Evaluation of the Montado Ecosystem Using Proximal Sensors

A Holistic Approach to the Evaluation of the Montado Ecosystem Using Proximal Sensors

Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

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