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Evaluation of the precision of the rising plate meter for measuring compressed sward height on heterogeneous grassland swards

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Abstract

Accurate estimation of herbage mass (HM) is essential for optimising grass utilisation and increasing profit for pasture-based livestock agriculture. The rising plate meter (RPM) is used for predicting HM based on average compressed sward height (CSH). Sampling resolution and distribution are primary parameters in determining spatial heterogeneity of HM. There is no definitive sampling protocol for the RPM. The objectives of this study were to: investigate spatial variation of HM within pastures, determine the number of RPM measurements required to accurately predict mean HM, and assess the precision of the RPM in terms of measurement repeatability. Intensive CSH measurements and HM reference cuts were carried out on controlled plots and grazed paddocks over two grazing seasons. Sward heterogeneity was estimated as the coefficient of variation (CV) of CSH and compared to empirically derived ‘true’ sward heterogeneity in terms of HM CV. Retrospective analysis simulations were performed to identify the effect of various reduced measurement resolutions on estimated mean CSH error. Repeated measures analysis was performed on grass samples to determine RPM measurement system precision. Results indicated that pasture heterogeneity varied by 36% across the growing season and was affected by grazing, fertilisation, sward composition and seasonality. Mean CSH could be estimated to within 5% relative prediction error by recording 24 measurements per ha in a random stratified manner. The standard deviation of RPM measurement repeatability was calculated to be 4.34 mm. The findings of this study will be used to inform the implementation of a more optimum grass measurement protocol.

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References

  • Bailey, J. S., Higgins, A., & Jordan, C. (2000). Empirical models for predicting the dry matter yield of grass silage swards using plant tissue analyses. Precision Agriculture, 2, 131–145.

    Google Scholar 

  • Barthram, G. T., Duff, E. I., Elston, D. A., Griffiths, J. H., Common, T. G., & Marriott, C. A. (2005). Frequency distributions of sward height under sheep grazing. Grass and Forage Science, 60, 4–16.

    Google Scholar 

  • Beecher, M., Hennessy, D., Boland, T. M., McEvoy, M., O’Donovan, M., & Lewis, E. (2015). The variation in morphology of perennial ryegrass cultivars throughout the grazing season and effects on organic matter digestibility. Grass and Forage Science, 70, 19–29.

    Google Scholar 

  • Beukes, P. C., McCarthy, S., Wims, C. M., Gregorini, P., & Romera, A. J. (2019). Regular estimates of herbage mass can improve profitability of pasture-based dairy systems. Animal Production Science, 59, 359–367.

    Google Scholar 

  • Black, A. D., Laidlaw, A. S., Moot, D. J., & O’Kiely, P. (2009). Comparative growth and management of white and red clovers. Irish Journal of Agricultural and Food Research, 48, 149–166.

    Google Scholar 

  • Bosker, T., Hoekstra, N. J., & Lantinga, E. A. (2003). The influence of feeding strategy on growth and rejection of herbage around dung pats and their decomposition. Journal of Agricultural Science, 139, 213–221.

    Google Scholar 

  • Castle, M. E. (1976). A simple disc instrument for estimating herbage yield. Grass and Forage Science, 31, 37–40.

    Google Scholar 

  • Cayley, J. W. D. & Bird, P. R. (1996a) General principles. In Techniques for measuring pastures (2nd ed., Vol. 2, pp. 1–5). Victoria, Australia: Pastoral and Veterinary Institute Hamilton.

  • Cayley, J. W. D. & Bird, P. R. (1996b). Measurement of pasture mass. In Techniques for measuring pastures (2nd ed., Vol. 2). Victoria, Australia: Pastoral and Veterinary Institute Hamilton.

  • Corral, J., & Fenlon, J. S. (1978). A comparative method for describing the seasonal distribution of production from grasses. Journal of Agricultral Science, 91, 61–67.

    Google Scholar 

  • Correll, O., Isselstein, J., & Pavlu, V. (2003). Studying spatial and temporal dynamics of sward structure at low stocking densities: The use of an extended rising-plate-meter method. Grass and Forage Science, 58, 450–454.

    Google Scholar 

  • Creighton, P., Kennedy, E., Shalloo, L., Boland, T. M., & O’ Donovan, M. (2011). A survey analysis of grassland dairy farming in Ireland, investigating grassland management, technology adoption and sward renewal. Grass and Forage Science, 66, 251–264.

    Google Scholar 

  • Delaby, L., Peyraud, J.-L., Bouttier, A., & Peccatte, J.-R. (1998). Effet d’une réduction simultanée de la fertilisation azotée et du chargement sur les performances des vaches laitières et la valorisation du pâturage (Effect of a simultaneous reduction of nitrogen fertilization and loading on the performance of dairy cows and the valuation of pasture). Ann Zootech, 47, 17–39.

    Google Scholar 

  • Dillon, P. (2006). Achieving high dry-matter intake from pasture with grazing dairy cow. In A. Elgersma, J. Dijkstra, & S. Tamminga (Eds.), Fresh Herbage for Dairy Cattle (pp. 1–26). Wageningen, The Netherlands: Springer.

    Google Scholar 

  • Dillon, P. (2011). The Irish dairy industry-Planning for 2020. In Proceedings of National Dairy Conference 2011 (pp. 1–24). Oak Park, Carlow, Ireland: Teagasc.

  • Dillon, P., Hennessy, T., Shalloo, L., Thorne, F., & Horan, B. (2008). Future outlook for the Irish dairy industry: A study of international competitiveness, influence of international trade reform and requirement for change. International Journal of Dairy Technology, 61, 16–29.

    Google Scholar 

  • Doole, G. J., & Romera, A. J. (2013). Detailed description of grazing systems using nonlinear optimisation methods: A model of a pasture-based New Zealand dairy farm. Agricultural Systems, 122, 33–41.

    Google Scholar 

  • Earle, D., & McGowan, A. (1979). Evaluation and calibration of an automated rising plate meter for estimating dry matter yield of pasture. Australian Journal of Experimental Agriculture and Animal Husbandry, 19, 337–343.

    Google Scholar 

  • Enriquez-Hidalgo, D., Gilliland, T. J., Egan, M., & Hennessy, D. (2018). Production and quality benefits of white clover inclusion into ryegrass swards at different nitrogen fertilizer rates. Journal of Agricultural Science, 156, 378–386.

    Google Scholar 

  • Evans, D. R., Williams, T. A., Jones, S., & Evans, S. A. (1998). The effect of cutting and intensive grazing managements on sward components of contrasting ryegrass and white clover types when grown in mixtures. Journal of Agricultural Science, 130, 317–322.

    Google Scholar 

  • Fehmi, J. S., & Stevens, J. M. (2009). A plate meter inadequately estimated herbage mass in a semi-arid grassland. Grass and Forage Science, 64, 322–327.

    Google Scholar 

  • Fernandez, A. R., & Rodriguez, A. G. (2013). Sward factors influence on pasture dry matter intake of grazing dairy cows: A review. Iranian Journal of Applied Animal Science, 3, 629–652.

    Google Scholar 

  • Ferraro, F. P., Nave, R. L. G., Sulc, R. M., & Barker, D. J. (2012). Seasonal variation in the rising plate meter calibration for forage mass. Agronomy Journal, 104, 1–6.

    Google Scholar 

  • Finneran, E., Crosson, P., O’Kiely, P., Shalloo, L., Forristal, D., & Wallace, M. (2012). Stochastic simulation of the cost of home-produced feeds for ruminant livestock systems. Journal of Agricultural Science, 150, 123–139.

    Google Scholar 

  • Hanrahan, L., Geoghegan, A., O’Donovan, M., Griffith, V., Ruelle, E., Wallace, M., et al. (2017). PastureBase Ireland: A grassland decision support system and national database. Computers and Electronics in Agriculture, 136, 193–201.

    Google Scholar 

  • Hanrahan, L., McHugh, N., Hennessy, T., Moran, B., Kearney, R., Wallace, M., et al. (2018). Factors associated with profitability in pasture-based systems of milk production. Journal of Dairy Science, 101, 5474–5485.

    CAS  PubMed  Google Scholar 

  • Haydock, K., & Shaw, N. (1975). The comparative yield method for estimating dry matter yield of pasture. Australian Journal of Experimental Agriculture and Animal Husbandry, 15, 663–670.

    Google Scholar 

  • Hirata, M. (2000). Quantifying spatial heterogeneity in herbage mass and consumption in pastures. Journal of Range Management, 53, 315–321.

    Google Scholar 

  • Humphreys, J. & Lawless, A. (2006). Moorepark dairy research update: A guide to management of white clover in grassland. Oak Park, Carlow, Ireland: Teagasc.

    Google Scholar 

  • Hunt, W. F. & Field, T. R. (1979). Growth characteristics of perennial rye grass. In Proceedings of New Zealand Grassland Association (pp. 104–113). Dunedin, New Zealnd: New Zealand Grassland Association.

  • Hurley, M. A., Boland, T. M., & Hennessy, D. (2018). Comparison of total mixed ration and pasture-based diets on dairy cow milk and milk solids yield. In Proceedings of Sustainable meat and milk production from grasslands (pp. 243–245). Moorepark, Cork, Ireland: The Organising Committee of the 27th General Meeting of the European Grassland Federation, Teagasc, Animal and Grassland Research and Innovation Centre.

  • Hurtado-Uria, C., Hennessy, D., Shalloo, L., Schulte, R. P. O., Delaby, L., & O’Connor, D. (2012). Evaluation of three grass growth models to predict grass growth in Ireland. Journal of Agricultural Science, 151, 91–104.

    Google Scholar 

  • Hutchinson, K. J., Scobie, D. R., Beautrais, J., Mackay, A. D., Rennie, G. M., Moss, R. A., et al. (2016). A protocol for sampling pastures in hill country. Journal of New Zealand Grasslands, 78, 203–210.

    Google Scholar 

  • Jordan, C., Shi, Z., Bailey, J. S., & Higgins, A. J. (2003). Sampling strategies for mapping ‘within-field’ variability in the dry matter yield and mineral nutrient status of forage grass crops in cool temperate climates. Precision Agriculture, 4, 69–86.

    Google Scholar 

  • Klootwijk, C. W., Holshof, G., de Boer, I. J. M., Van den Pol-Van Dasselaar, A., Engel, B., & Van Middelaar, C. E. (2019). Correcting fresh grass allowance for rejected patches due to excreta in intensive grazing systems for dairy cows. Journal of Dairy Science, 102, 10451–10459.

    CAS  PubMed  Google Scholar 

  • Klootwijk, C. W., Holshof, G., Van den Pol-Van Dasselaar, A., van Helvoort, K. L. M., Engel, B., de Boer, I. J. M., et al. (2019). The effect of intensive grazing systems on the rising plate meter calibration for perennial ryegrass pastures. Jornal of Dairy Science, 102, 10439–10450.

    CAS  Google Scholar 

  • L'Huillier, P. J. & Thomson, N. A. (1988). Estimation of herbage mass in ryegrass/white clover dairy pastures. In Proceedings of New Zealand Grassland Association (pp. 117–122). Dunedin, New Zealand: New Zealand Grassland Association.

  • Lile, J., Blackwell, M., Thomson, N., Penno, J., Macdonald, K., Nicholas, P., et al. (2001). Practical use of the rising plate meter (RPM) on New Zealand dairy farms. In Proceedings of the New Zealand Grassland Association (pp. 159–164). Dunedin, New Zealand: New Zealand Grassland Association.

  • Maher, J. & Bogue, F. (2018). Grass 10: An extension campaign to improve the level of grass production and utilisation. In Proceedings of Sustainable meat and milk production from grasslands 27th General Meeting of the European Grassland Federation (pp. 956–958). Moorepark, Cork, Ireland: The Organising Committee of the 27th General Meeting of the European Grassland Federation, Teagasc, Animal and Grassland Research and Innovation Centre.

  • Mannetje, L. T. (2000). Measuring biomass of grassland vegetation. In L. ’tMannetje & R. M. Jones (Eds.), Field and laboratory methods for grassland and animal production research (pp. 151–177). The Netherlands: Wageningen University.

    Google Scholar 

  • Martin, R. C., Astatkie, T., Cooper, J. M., & Fredeen, A. H. (2005). A comparison of methods used to determine biomass on naturalized swards. Journal of Agronomy and Crop Science, 191, 152–160.

    Google Scholar 

  • Mawby, W. D. (2006). Make your destructive, dynamic, and attribute measurement system work for you. Milwaukee, WI, USA: ASQ Quality Press.

    Google Scholar 

  • McEvoy, M., O’Donovan, M., & Shalloo, L. (2011). Development and application of an economic ranking index for perennial ryegrass cultivars. Journal of Dairy Science, 94, 1627–1639.

    CAS  PubMed  Google Scholar 

  • McSweeney, D., Coughlan, N. E., Cuthbert, R. N., Halton, P., & Ivanov, S. (2019). Micro-sonic sensor technology enables enhanced grass height measurement by a Rising Plate Meter. Information Processing in Agriculture, 6, 279–284.

    Google Scholar 

  • Menneer, J., Ledgard, S., McLay, C., & Silvester, W. (2005). The effects of treading by dairy cows during wet soil conditions on white clover productivity, growth and morphology in a white clover–perennial ryegrass pasture. Grass and Forage Science, 60, 46–58.

    Google Scholar 

  • Met Éireann. (2018). Warm dry weather of June and July 2018, Dublin, Ireland: Met Éireann. Retrieved December 12, 2019 from https://www.met.ie/warm-and-dry-weather-of-june-and-july

  • Michell, P. (1982). Value of a rising-plate meter for estimating herbage mass of grazed perennial ryegrass-white clover swards. Grass and Forage Science, 37, 81–87.

    Google Scholar 

  • Microsoft. (2010). Visual Basics for Applications 7. Redmond WA, USA: Microsoft. Retrieved December 13, 2016, from https://www.microsoft.com/en-ie/microsoft-365/p/excel

  • Nakagami, K. (2016). A method for approximate on-farm estimation of herbage mass by using two assessments per pasture. Grass and Forage Science, 71, 490–496.

    Google Scholar 

  • Odonovan, M., Dillon, P., Rath, M., & Stakelum, G. (2002). A comparison of four methods of herbage mass estimation. Irish Journal of Agriculture and Food Research, 41, 17–27.

    Google Scholar 

  • O’Neill, B. F., Lewis, E., O’Donovan, M., Shalloo, L., Mulligan, F. J., Boland, T. M., et al. (2013). Evaluation of the GrazeIn model of grass dry-matter intake and milk production prediction for dairy cows in temperate grass-based production systems. 1-Sward characteristics and grazing management factors. Grass and Forage Science, 68, 504–523.

    CAS  Google Scholar 

  • Peyraud, J., Mosquera-Losada, R., & Delaby, L. (2004). Challenges and tools to develop efficient dairy systems based on grazing: How to meet animal performance and grazing management. Grassland Science in Europe, 9, 373–384.

    Google Scholar 

  • QGIS Development Team. (2018). QGIS Geographic Information System version 2.18.16. NC, USA: Open Source Geospatial Foundation Project. Retrieved February 14, 2018, from https://qgis.org

  • R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved August 15, 2018 from https://www.r-project.org/

  • Sanderson, M. A., Rotz, C. A., Fultz, S. W., & Rayburn, E. B. (2001). Estimating forage mass with a commercial capacitance meter, rising plate meter, and pasture ruler. Agronomy Journal, 93, 1281–1286.

    Google Scholar 

  • Stevens, D. L., & Olsen, A. R. (2004). Spatially balanced sampling of natural resources. Journal of the American Statistical Association, 99, 262–278.

    Google Scholar 

  • Taylor, B. N. & Kuyatt, C. E. (1994). Guidelines for evaluating and expressing the uncertainty of NIST measurement results. Gaithersburg, MD, USA: National Institute of Standards and Technology Technical Note 1297.

  • Thomson, N. A. (1983). Factors influencing the accuracy of herbage mass determinations with a capacitance meter. New Zealand Journal Experimental Agriculture, 11, 171–176.

    Google Scholar 

  • Thomson, N. A., Mccallum, D. A., Howse, S., Holmes, C. W., Mathews, P. N. P., & Mathews, C. (1997). Estimation of dairy pastures: The need for standardisation. In Proceedings of the New Zealand Grassland Association (pp. 221–225). Dunedin, New Zealand: New Zealand Grassland Association.

  • Thomson, N. A., Upsdell, M., Hooper, R., Henderson, H., Blackwell, M. B., Mccallum, D. A et al. (2001). Development and evaluation of a standardised means for estimating herbage mass of dairy pastures using the rising plate meter. In Proceedings of New Zealand Grassland Association (pp. 149–157). Dunedin, New Zealand: New Zealand Grassland Association.

  • Tukey, J. W. (1977). Exploratory data analysis (Vol. 2). Reading, MA, USA: Addison-Wesley.

    Google Scholar 

  • Verwer, C., Schooten, H. V., Philipsen, B., Lennsinck, F., Houwelingen, K. V., & Eekeren, N. V. (2016). Rejection of grass around dung pats; influence of smell, taste or both? In Proceedings of the 26th General Meeting of the European Grassland Federation (pp. 430–432). Ås, Akershus, Norway: Organising Committee of the 26th General Meeting of the European Grassland Federation, NIBIO.

  • Webster, R. & Lark, M. (2012a). Efficiency, economy and logistics. Field sampling for environmental science and management (pp. 56–58). Oxon, UK: Routledge.

  • Webster, R. & Lark, M. (2012b). Simple random sampling. Field sampling for environmental science and management. Oxon, UK: Routledge.

  • Wright, A. (2002). Measuring biomass in grassland vegetation. In L. ’tMannetje & R. M. Jones (Eds.), Field and laboratory methods for grassland and animal production research (pp. 151–177). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Zegler, C. H., Renz, M. J., Brink, G. E., & Ruark, M. D. (2020). Assessing the importance of plant, soil, and management factors affecting potential milk production on organic pastures using regression tree analysis. Agricultural Systems, 180, 102776.

    Google Scholar 

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Acknowledgements

The authors would like to thank the staff and students of the Moorepark Grassland Laboratory for their hard work and dedication during the course of this study. This research was supported by the Irish Department of Agriculture, Food and the Marine and the European Commission’s ERA-NET, ICT – AGRI scheme (Grant No. 15ICTAGRI_1) as part of the Horizon 2020 project.

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This research was supported by the Irish Department of Agriculture, Food and the Marine and the European Commission’s ERA-NET, ICT – AGRI scheme as part of the Horizon 2020 project.

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  1. Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Co., Cork, Ireland

    D. J. Murphy, B. O’ Brien, D. Hennessy & M. Hurley

  2. Department of Process, Energy and Transport Engineering, Cork Institute of Technology, Cork, Ireland

    D. J. Murphy & M. D. Murphy

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Murphy, D.J., O’ Brien, B., Hennessy, D. et al. Evaluation of the precision of the rising plate meter for measuring compressed sward height on heterogeneous grassland swards. Precision Agric 22, 922–946 (2021). https://doi.org/10.1007/s11119-020-09765-9

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