Dynamic algorithmic conversion of compressed sward height to dry matter yield by a rising plate meter
Coughlan, Neil E.
KeywordDigital data capture
Dry matter yield
Rising plate meter
MetadataShow full item record
StatisticsDisplay Item Statistics
CitationDiarmuid McSweeney, Luc Delaby, Bernadette O'Brien, Alexis Ferard, Nicky Byrne, Justin McDonagh, Stepan Ivanov, Neil E. Coughlan, Dynamic algorithmic conversion of compressed sward height to dry matter yield by a rising plate meter, Computers and Electronics in Agriculture, Volume 196, 2022, 106919, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2022.106919.
AbstractThe strategic allocation of pasture grazing area to dairy cows is essential for optimal management and increased outputs. Rising plate meters are frequently used to estimate pasture herbage mass, i.e. dry matter yield per hectare, by employing simple regression equations that relate compressed sward height to herbage mass. However, 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. Using a total of 308 grass plots, the variation of growth for both perennial ryegrass and hybrid ryegrass was recorded over the seven-month growing season, i.e. March–September. From these data three dynamic equations were derived. The models showed reduced levels of error in comparison to most other conventional equations. As such, the derived models represent a considerable advance for predictive assessment of herbage mass and will support more efficient grassland utilisation by farmers. Although all equations were found to be highly accurate and precise, only a single equation was considered the most effective (R2 = 0.7; RMSE = 248.05), allowing herbage mass to be predicted reliably from compressed sward height data in relation to ryegrass type and calendar month. Although further research will be required, the results presented allow farm operators to calculate herbage mass, as well as support the development of decision support tools to improve on-farm grassland management, particularly at the local paddock rather than national level.
FunderEuropean Union FP7 Era-net of ICT GRAZINGTOOLS
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as © 2022 The Authors. Published by Elsevier B.V.