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Evaluation of three grass growth models to predict grass growth in Ireland

Published online by Cambridge University Press:  13 April 2012

C. HURTADO-URIA ,
D. HENNESSY ,
L. SHALLOO ,
R. P. O. SCHULTE ,
L. DELABY  and
D. O'CONNOR
C. HURTADO-URIA
Affiliation:
Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland Cork Institute of Technology, Bishopstown, Cork, Ireland
D. HENNESSY*
Affiliation:
Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
L. SHALLOO
Affiliation:
Teagasc, Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
R. P. O. SCHULTE
Affiliation:
Environment Research Department, Crops Environment and Land Use Programme, Teagasc, Johnstown Castle, Co. Wexford, Ireland
L. DELABY
Affiliation:
INRA, AgroCampus Ouest, UMR 1348, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage, F-35590 Saint-Gilles, France
D. O'CONNOR
Affiliation:
Cork Institute of Technology, Bishopstown, Cork, Ireland
*
*To whom all correspondence should be addressed. Email:[email protected]
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Summary

Grass growth in temperate regions is highly seasonal and difficult to predict. A model that can predict grass growth from week to week would offer a valuable management and budgeting tool for grassland farmers. Many grass growth models have been developed, varying from simple empirical to complex mechanistic models. Three published grass growth models developed for perennial ryegrass swards in temperate climates were selected for evaluation: Johnson & Thornley (1983) (J&T model), Jouven et al. (2006) (J model) and Brereton et al. (1996) (B model). The models were evaluated using meteorological data and grass growth data from Teagasc Moorepark as a framework for further refinement for Irish conditions. The accuracy of prediction by the models was assessed using root mean square error (RMSE) and mean square prediction error (MSPE). The J&T model over-predicted grass growth in all 5 years examined and predicted a high primary grass growth peak, while the J and B models predicted grass growth closer to that measured. Overall, the J model had the smallest RMSE in 3 of the 5 years and the B model in 2 of the 5 years. In spring (February–April), the B model had the lowest RMSE and MSPE. In mid-season (April–August), the B model had the closest prediction to measured data (lowest RMSE), while in autumn (August–October) the J model had the closest prediction. The models with the greatest potential for grass growth prediction in Ireland, albeit with some modifications, are the J model and the B model.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 151 , Issue 1 , February 2013 , pp. 91 - 104
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Barrett, P. D., Laidlaw, A. S. & Mayne, C. S. (2004). An evaluation of selected perennial ryegrass growth models for development and integration into a pasture management decision support system. Journal of Agricultural Science, Cambridge 142, 327–334.CrossRefGoogle Scholar
Bélanger, G., Gastal, F. & Warembourg, F. R. (1994). Carbon balance of tall fescue (Festuca arundinacea Schreb.): effects of nitrogen fertilization and the growing season. Annals of Botany, 74, 653–659.CrossRefGoogle Scholar
Brereton, A. J. (1992). The impact of weather on grassland farming. In Irish Farming, Weather and Environment (Ed. Keane, T.), pp. 125–135. Dublin: Agmet.Google Scholar
Brereton, A. J., Danielov, S. A. & Scott, D. (1996). Agrometeorology of Grass and Grasslands for Middle Latitudes. Technical Note No. 197. Geneva: World Meteorological Organisation.Google Scholar
Brereton, A. J. & O'Riordan, E. (2001). A comparison of grass growth models. In Agro-meteorological Modelling – Principles, Data and Applications (Ed. Holden, N. M.), pp. 136–154. Dublin: Agmet.Google Scholar
Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., Bussière, F., Cabidoche, Y. M., Cellier, P., Debaeke, P., Gaudillère, J. P., Hénault, C., Maraux, F., Seguin, B. & Sinoquet, H. (2003). An overview of the crop model STICS. European Journal of Agronomy 18, 309–332.CrossRefGoogle Scholar
Burke, J. I., Brereton, A. J., O'Kiely, P. & Schulte, R. P. O. (2004). Weather and crop production. In Climate, Weather and Irish Agriculture (Eds Keane, T. & Collins, J. F.), pp. 161–210. Dublin: Agmet.Google Scholar
Corral, A. J. & Fenlon, J. S. (1978). A comparative method for describing the seasonal distribution of production from grasses. Journal of Agricultural Science, Cambridge 91, 61–67.CrossRefGoogle Scholar
Cruz, P., Duru, M., Therond, O., Theau, J. P., Ducourtieux, C., Jouany, C., Al Haj Khaled, R. & Ansquer, P. (2002). Une nouvelle approche pour caractériser les prairies naturelles et leur valeur d'usage. Fourrages 172, 335–354.Google Scholar
Davies, A. (1977). Structure of the grass sward. In Proceedings of an International Meeting on Animal Production from Temperate Grassland (Ed. Gilsenan, B.), pp. 36–44. Dublin: An Foras Taluntais.Google Scholar
Dillon, P., Roche, J. R., Shalloo, L. & Horan, B. (2005). Optimising financial return from grazing in temperate pastures. In Utilisation of Grazed Grass in Temperate Animal Systems (Ed. Murphy, J. J.), pp. 131–147. Wageningen, The Netherlands: Wageningen Academic Publishers.CrossRefGoogle Scholar
Finneran, E., Crosson, P., O'Kiely, P., Shalloo, L., Forristal, P. D. & Wallace, M. (2010). Simulation modelling of the cost of producing and utilizing feeds for ruminants on Irish farms. Journal of Farm Management 14, 95–116.Google Scholar
Garcia-launay, F., Sibra, C., Molénat, H., Agabriel, C. & Brunschwig, G. (2012). Grassland use in mountain bovine systems according to a hierarchy of geographical determinants. The Journal of Agricultural Science, Cambridge 150, 203–217.CrossRefGoogle Scholar
Grogan, D. & Gilliland, T. J. (2010). A review of perennial ryegrass variety evaluation in Ireland. In Grasses for the Future: Proceedings of a Conference held at Silver Springs and Moorepark, Cork, Ireland, 14 Oct 2010 (Eds O'Donovan, M. & Hennessy, D.), pp. 99–116. Oak Park, Carlow, Ireland: Teagasc.Google Scholar
Hennessy, D. (2005). Manipulation of grass supply to meet feed demand of beef cattle and dairy cows. PhD thesis, Queen's University, Belfast, United Kingdom.Google Scholar
Hennessy, D., O'Donovan, M., French, P. & Laidlaw, A. S. (2006). Effects of date of autumn closing and timing of winter grazing on herbage production in winter and spring. Grass and Forage Science 61, 363–374.CrossRefGoogle Scholar
Hennessy, D., O'Donovan, M., French, P. & Laidlaw, A. S. (2008). Factors influencing tissue turnover during winter in perennial ryegrass-dominated swards. Grass and Forage Science 63, 202–211.CrossRefGoogle Scholar
Holden, N. M. (2001). Modelling concepts. In Agro-meteorological Modelling: Principles, Data and Applications (Ed. Holden, N. M.), pp. 1–22. Dublin: Agmet.Google Scholar
Hopkins, A. (2000). Introduction. In Grass: Its Production and Utilization, 3rd edn (Ed. Hopkins, A.), pp. 1–12. Oxford, UK:Blackwell Science.Google Scholar
Houlbrooke, D. J., Paton, R. J., Littlejohn, R. P. & Morton, J. D. (2011). Land-use intensification in New Zealand: effects on soil properties and pasture production. Journal of Agricultural Science, Cambridge 149, 337–349.CrossRefGoogle Scholar
Hurley, G., Gilliland, T. J. & O'Donovan, M. (2008). Relationship between reproductive initiation and ear emergence development in Lolium perenne L. Journal of Agricultural Science, Cambridge 146, 655–665.CrossRefGoogle Scholar
Jewiss, O. R. (1993). Shoot development and number. In Sward Measurement Handbook, 2nd edn (Eds Davies, A., Baker, R. D., Grant, S. A. & Laidlaw, A. S.), pp. 99–120. Reading, UK: The British Grassland Society.Google Scholar
Jin, Z., Yezheng, W. & Gang, Y. (2005). General formula for estimation of monthly average daily solar radiation in China. Energy Conversion and Management, 46, 257–268.CrossRefGoogle Scholar
Johnson, I. R. & Thornley, J. H. M. (1983). Vegetative crop growth model incorporating leaf area expansion and senescence, and applied to grass. Plant, Cell and Environment 6, 721–729.CrossRefGoogle Scholar
Jouven, M., Carrère, P. & Baumont, R. (2006). Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 1. Model description. Grass and Forage Science 61, 112–124.CrossRefGoogle Scholar
Keane, T. & Collins, J. F. (2004). Climate, Weather and Irish Agriculture. Dublin: Agmet.Google Scholar
Kennedy, E., O'Donovan, M., Murphy, J. P., Delaby, L. & O'Mara, F. P. (2007). Effect of spring grazing date and stocking rate on sward characteristics and dairy cow production during midlactation. Journal of Dairy Science 90, 2035–2046.CrossRefGoogle ScholarPubMed
Laidlaw, A. S. & Mayne, C. S. (2000). Setting management limits for the production and utilization of herbage for out-of-season grazing. Grass and Forage Science 55, 14–25.CrossRefGoogle Scholar
Leafe, E. L., Stiles, W. & Dickinson, S. E. (1975). Physiological processes influencing the pattern of productivity of the intensively managed grass sward. In Proceedings of the 12th International Grassland Congress (Eds Iglovikov, V. G. & Movsissyants, A. P.), pp. 442–457. Moscow: International Grassland Congress.Google Scholar
McEvoy, M., O'Donovan, M. & Shalloo, L. (2011). Development and application of an economic ranking index for perennial ryegrass varieties. Journal of Dairy Science 94, 1627–1639.CrossRefGoogle Scholar
O'Kiely, P. (1994). The Cost of Feedstuffs for Cattle. Technical bulletin No. 6. Dublin: R. & H. Hall.Google Scholar
Parsons, A. J. & Chapman, D. F. (2000). The principles of pasture growth. In Grass: Its Production and Utilization, 3rd edn (Ed. Hopkins, A.), pp. 31–89. Oxford, UK: Blackwell Science Ltd.Google Scholar
Rook, A. J., Dhanoa, M. S. & Gill, M. (1990). Prediction of the voluntary intake of grass silages by beef cattle. 3. Precision of alternative prediction models. Animal Production 50, 455–466.Google Scholar
Schapendonk, A. H. C. M., Stol, W., Van Kraalingen, D. W. G. & Bouman, B. A. M. (1998). LINGRA, a sink/source model to simulate grassland productivity in Europe. European Journal of Agronomy 9, 87–100.CrossRefGoogle Scholar
Schulte, R. P. O. (2005). A new agro-meteorological simulation model for predicting daily grass growth rates across Ireland (abstract). In Utilisation of Grazed Grass in Temperate Animal Systems (Ed. Murphy, J. J.), p. 203. Wageningen, The Netherlands: Wageningen Academic Publishers.CrossRefGoogle Scholar
Schulte, R. P. O., Diamond, J., Finkele, K., Holden, N. M. & Brereton, A. J. (2005). Predicting the soil moisture conditions of Irish grasslands. Irish Journal of Agricultural and Food Research 44, 95–110.Google Scholar
Schulte, R. P. O., Richards, K., Daly, K., Kurz, I., McDonald, E. J. & Holden, N. M. (2006). Agriculture, meteorology and water quality in Ireland: a regional evaluation of pressures and pathways of nutrient loss to water. Biology and Environment 106, 117–134.CrossRefGoogle Scholar
Smith, L. P. (1967). Potential Transpiration for use in Irrigation and Hydrology in the United Kingdom and Republic of Ireland. MAFF Technical Bulletin No. 16. London: HMSO.Google Scholar
Thornley, J. H. M. (1998). Grasslands Dynamics: An Ecosystem Simulation Model. Wallingford, UK: CABI.CrossRefGoogle Scholar
Trnka, M., Eitzinger, J., Dubrovský, M., Semerádová, D., Štěpánek, P., Hlavinka, P., Balek, J., Skalák, P., Farda, A., Formayer, H. & Žalud, Z. (2010). Is rainfed crop production in central Europe at risk? Using a regional climate model to produce high resolution agroclimatic information for decision makers. Journal of Agricultural Science, Cambridge 148, 639–656.CrossRefGoogle Scholar
Woodward, S. J. R. (2001). Validating a model that predicts daily growth and feed quality of New Zealand dairy pastures. Environment International 27, 133–137.CrossRefGoogle Scholar