The Use of ICT Tools to Capture Grass Data and Optimise Grazing Management

McSweeney, Diarmuid (2019) The Use of ICT Tools to Capture Grass Data and Optimise Grazing Management. Doctoral thesis, Waterford Institute of Technology.

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Abstract

In temperate regions, where pasture-based milk production systems predominate, the strategic allocation of pasture grazing area to dairy cows is essential for optimal management and increased milk outputs. Rising plate meters (RPM) are frequently used to estimate pasture herbage mass (HM; i.e. dry matter yield per hectare), through the use of simple regression equations that relate compressed sward height (CSH) to HM. Measurement must be accurate and efficient. Despite improved farm management practices aided by a variety of technological advances, the standard design of a RPM has remained relatively unchanged. As part of this thesis, a RPM utilising a micro-sonic sensor and digital data capture capability linked to a smart device application was developed. Further, the ability of the micro-sonic sensor RPM, to accurately and precisely measure fixed heights was examined. As correct allocation of grazing area requires accurate geolocation positioning, the associated GPS technology was assessed. In order to improve the accuracy and precision of these equations, so that inherent variation of grasslands is captured, there is a need to incorporate differences in grass types and seasonal growth As good bassline data are required for the development of effective conversion of CSH to HM, the variation of growth for both perennial ryegrass and hybrid ryegrass was recorded over the seven month growing season, using a total of 308 grass plots. Once the correct HM is established it must be allocated to the herd in an accurate and efficient manner. As intensive pasture-based farming systems rely on precise and frequent allocations of grass to animals, a Virtual Fence (VF) system to enhance automated allocation of correct forage areas to animals was developed and assessed, as was an associated cow training protocol. The micro-sonic sensor RPM was found to be significantly more accurate for height capture than a traditional ratchet counter RPM. The ratchet counter RPM underestimated height by 7.68 ± 0.06 mm (mean ± SE), while the micro-sonic sensor RPM overestimated height by 0.18 ± 0.08 mm. These discrepancies can result in an under- and overestimation of HM by 13.71 % and 0.32 % per Ha-1, respectively. The performance of the on-board GPS did not significantly differ from that of a tertiary device. Subsequently, three dynamic equations were derived for the effective conversion algorithms form CSH to HM incorporating different grass types, time of the year and dry matter percentage, one of algorithms is now in everyday commercial use. Although the operating capacity of the VF system was found to be robust, with dairy cows rapidly associating visual cues with VF boundary lines, and a cue-consequence association with the audio warning and corrective stimulus, the number of boundary challenges made by cows increased upon removal of all visual cues. Overall, although further research will be required, the results presented within this thesis allow for the further development of decision support tools to improve on-farm grassland management.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Grass Data, Grazing Management
Departments or Groups:	*NONE OF THESE*
Divisions:	School of Science > Department of Computing, Maths and Physics
Depositing User:	Derek Langford
Date Deposited:	14 Oct 2019 13:33
Last Modified:	14 Oct 2019 13:33
URI:	https://repository.wit.ie/id/eprint/3386

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