Development of a Grass Measurement Optimisation Tool to Efficiently Measure Herbage Mass on Grazed Pastures

Authors

D. J. Murphy, Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co., Cork, Ireland; Department of Process, Energy & Transport Engineering, Cork Institute of Technology, Cork, Ireland
B. O'Brien, Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co., Cork, Ireland
Michael D. Murphy, Department of Process, Energy & Transport Engineering, Cork Institute of Technology, Cork, IrelandFollow

Document Type

Article

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Disciplines

Agriculture | Bioresource and Agricultural Engineering | Dairy Science | Engineering

Publication Details

Computers and Electronics in Agriculture

Abstract

Accurate and efficient estimation of herbage mass is essential for optimising grass utilisation and increasing profit for pasture farming. There is no definitive sampling protocol for grass measurement on Irish pastures. This paper presents the Grass Measurement Optimisation Tool (GMOT), designed to generate measurement protocols that optimise for time and accuracy. The GMOT was designed in the form of a decision support tool that generates interactive paddock maps that guide the farmer on how to optimally measure their pastures in a random stratified manner based on GPS co-ordinates, resulting in accurate non-biased estimations of mean herbage mass. Rising plate meter (RPM) measurements and reference herbage cuts were performed on trial plots and grazed paddocks over three years. Measurement routes were optimised using a genetic algorithm based on a traveling salesman problem. Actual survey error was estimated in terms of relative prediction error using Monte Carlo simulations that combined measurement and calibration error distributions for the RPM. Cost benefit analysis was conducted to evaluate the feasibility of using the GMOT on Irish grasslands. Actual error for the RPM decreased from 37% to 26% as measurement rates increased from 1 to 8 ha−1 and reductions in error were negligible (<1%) as measurements increased from 8 to 32 ha−1. Calibration error was the largest source of error (25.9%) compared to measurement error (8%). Optimal measurement value was achieved by performing 8 measures ha−1 and further increasing the measurement rate resulted in diminishing returns. The GMOT is compatible with a range of pasture measurement technologies.

Recommended Citation

D.J. Murphy, B. O' Brien, M.D. Murphy, Development of a grass measurement optimisation tool to efficiently measure herbage mass on grazed pastures, Computers and Electronics in Agriculture, Volume 178, 2020, https://doi.org/10.1016/j.compag.2020.105799.