Modelling Pasture-based Automatic Milking System Herds: Grazeable Forage Options

  • Islam, M.R. (Dairy Science Group, Faculty of Veterinary Science, The University of Sydney) ;
  • Garcia, S.C. (Dairy Science Group, Faculty of Veterinary Science, The University of Sydney) ;
  • Clark, C.E.F. (Dairy Science Group, Faculty of Veterinary Science, The University of Sydney) ;
  • Kerrisk, K.L. (Dairy Science Group, Faculty of Veterinary Science, The University of Sydney)
  • Received : 2014.05.21
  • Accepted : 2014.09.09
  • Published : 2015.05.01


One of the challenges to increase milk production in a large pasture-based herd with an automatic milking system (AMS) is to grow forages within a 1- km radius, as increases in walking distance increases milking interval and reduces yield. The main objective of this study was to explore sustainable forage option technologies that can supply high amount of grazeable forages for AMS herds using the Agricultural Production Systems Simulator (APSIM) model. Three different basic simulation scenarios (with irrigation) were carried out using forage crops (namely maize, soybean and sorghum) for the spring-summer period. Subsequent crops in the three scenarios were forage rape over-sown with ryegrass. Each individual simulation was run using actual climatic records for the period from 1900 to 2010. Simulated highest forage yields in maize, soybean and sorghum- (each followed by forage rape-ryegrass) based rotations were 28.2, 22.9, and 19.3 t dry matter/ha, respectively. The simulations suggested that the irrigation requirement could increase by up to 18%, 16%, and 17% respectively in those rotations in El-Nino years compared to neutral years. On the other hand, irrigation requirement could increase by up to 25%, 23%, and 32% in maize, soybean and sorghum based rotations in El-Nino years compared to La-Nina years. However, irrigation requirement could decrease by up to 8%, 7%, and 13% in maize, soybean and sorghum based rotations in La-Nina years compared to neutral years. The major implication of this study is that APSIM models have potentials in devising preferred forage options to maximise grazeable forage yield which may create the opportunity to grow more forage in small areas around the AMS which in turn will minimise walking distance and milking interval and thus increase milk production. Our analyses also suggest that simulation analysis may provide decision support during climatic uncertainty.


  1. Andrade, F. H. 1995. Analysis of growth and yield of maize, sunflower and soybean grown at Balcarce, Argentina. Field Crops Res. 41:1-12.
  2. Andrade, F. H., S. A. Uhart, and A. Cirilo. 1993. Temperature affects radiation use efficiency in maize. Field Crops Res. 32:17-25.
  3. Anwar, M. R., G. J. O'Leary, M. A. Rab, P. D. Fisher, and R. D. Armstrong. 2009. Advances in precision agriculture in southeastern Australia. V. Effect of seasonal conditions on wheat and barley yield response to applied nitrogen across management zones. Crop Pasture Sci. 60:901-911.
  4. Awal, M. A., H. Koshi, and T. Ikeda. 2006. Radiation interception and use by maize/peanut intercrop canopy. Agric. For. Meteorol. 139:74-83.
  5. Carberry, P. S., S. G. K. Adiku, R. L. McCown, and B. A. Keating. 1996. Application of the APSIM cropping systems model to intercropping systems. In: Dynamics of Roots and Nitrogen in Cropping Systems of the Semi-Arid Tropics (Eds O. Ito, C. Johansen, J. J. Adu-Gyamfi, K. Katayama, J. V. D. K. Kumar Rao, T. J. Rego) Japan International Research Centre for Agricultural Sciences, Ibaraki, Japan. pp. 637-648.
  6. Caviglia, O. P., V. O. Sadras, and F. H. Andrade. 2004. Intensification of agriculture in the south-eastern Pampas I. Capture and efficiency in the use of water and radiation in double-cropped wheat-soybean. Field Crops Res. 87:117-129.
  7. Chauhan, Y. S. 2010. Potential productivity and water requirements of maize-peanut rotations in Australian semiarid tropical environments-A crop simulation study. Agric. Water Manag. 97:457-464.
  8. Donohue, R. H., K. L. Kerrisk, S. C. Garcia, D. A. Dickeson, and P. C. Thomson. 2010. Evaluation of two training programs aimed to improve early lactation performance of heifers in a pasturebased automated milking system. Anim. Prod. Sci. 50:939-945.
  9. Echarte, L., A. Della Maggiora, D. Cerrudo, V. H. Gonzalez, P. Abbate, A. Cerrudo, V. O. Sadras, and P. Calvino. 2011. Yield response to plant density of maize and sunflower intercropped with soybean. Field Crops Res. 121:423-429.
  10. Farina, S. R., S. C. Garcia, and W. J. Fulkerson. 2011. A complementary forage system whole-farm study: forage utilization and milk production. Anim. Prod. Sci. 51:460-470.
  11. Fulkerson, W. J., A. Horadagoda, J. S. Neal, I. Barchia, and K. S. Nandra. 2008. Nutritive value of forage species grown in the warm temperate climate of Australia for dairy cows: Herbs and grain crops. Livest. Sci. 114:75-83.
  12. Garcia, S. C. and W. J. Fulkerson. 2005. Opportunities for future Australian dairy systems: A review. Aust. J. Exp. Agric. 45:1041-1055.
  13. Garcia, S. C., W. J. Fulkerson, R. Nettle, S. Kenny, and D. Armstrong. 2007a. FutureDairy: A national multidisciplinary project to assist dairy farmers to manage challenges - methods and early findings. Aust. J. Exp. Agric. 47:1025-1031.
  14. Garcia, S. C., J. L. Jacobs, S. L. Woodward, and D. A. Clark. 2007b. Complementary forage rotation: A review. In: Meeting the Challenges for Pasture Based Dairying, Proceedings of the Australian Dairy Science Symposium (Eds. D. F. Chapman, D. A. Clark, K. L. Macmillan, and D. P. Nation). National Dairy Alliance, Melbourne, Australia. pp. 221-239.
  15. Garcia, S. C., W. J. Fulkerson, and S. U. Brookes. 2008. Dry matter production, nutritive value and efficiency of nutrient utilization of a complementary forage rotation compared to a grass pasture system. Grass Forage Sci. 63:284-300.
  16. Isbell, R. F. 2002. Australian Soil Classification. CSIRO Publishing, Collingwood, Victoria, Australia.
  17. Islam, M. R. and S. C. Garcia. 2009. Forage option to increase forage and water productivity in autumn-winter. 14th Dairy Sci. Symp., The University of Sydney, Sydney, Australia. pp. 114-116.
  18. Islam, M. R. and S. C. Garcia. 2010. Simulation of a triple-crop complementary forage rotation using APSIM. FutureDairy 2 Milestone Report 12, Modelling Studies Report, July 2010, Milestone report to Dairy Australia, Vic., Australia. pp. 7-19.
  19. Islam, M. R. and S. C. Garcia. 2012. Effects of sowing date and nitrogen fertilizer on forage yield, nitrogen- and water-use efficiency and nutritive value of an annual triple-crop complementary forage rotation. Grass Forage Sci. 67:96-110.
  20. Islam, M. R., S. C. Garcia, and A. Horadagoda. 2012. Effects of residual nitrogen, nitrogen fertilizer, sowing data and harvest time on yield and nutritive value of forage rape. Anim. Feed Sci. Technol. 177:52-64.
  21. Islam, M. R. and S. C. Garcia. 2013. Forage options for dairy cows. 22nd Int. Grassl. Cong., 15-19 September, Sydney, Australia. pp. 1719-1720.
  22. Jago, J. G., K. Bright, P. Copeman, K. Davis, A. K. Jackson, I. Ohnstad, R. Wieliczko, and M. Woolford. 2004. Remote automatic selection for cows for milking in a pasture-based automatic milking system. Proc. NZ Soc. Anim. Prod. 64:241-245.
  23. Jago, J. G., K. L. Davis, P. J. Copeman, I. Ohnstad, and M. M. Woolford. 2007. Supplementary feeding at milking and minimum milking interval effects on cow traffic and milking performance in a pasture-based automatic milking system. J. Dairy Res. 74:492-499.
  24. Jago, J. G. and K. L. Kerrisk. 2011. Training methods for introducing cows to a pasture-based automatic milking system. Appl. Anim. Behav. Sci. 131:79-85.
  25. Keating, B. A., P. S. Carberry, G. L. Hammer, M. E. Probert, M. J. Robertson, D. Holzworth, N. I. Huth, J. N. G. Hargreaves, H. Meinke, Z. Hochman, G. McLean, K. Verburg, V. Snow, J. P. Dimes, M. Silburn, E. Wang, S. Brown, K. L. Bristow, S. Asseng, S. Chapman, R. I. McCown, D. M. Freebairn, and C. J. Smith. 2003. An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron. 18:267-288.
  26. Kolbach, R., K. L. Kerrisk, S. C. Garcia, and N. Dhand. 2012. Attachment accuracy of a novel prototype robotic rotary and investigation of two management strategies for incomplete milk quarters. Comput. Electron. Agric. 88:120-124.
  27. Lyons, N. A., K. L. Kerrisk, and S. C. Garcia. 2014. Milking frequency management in pasture-based automatic milking system: A review. Livest. Sci. 159:102-116.
  28. Neal, J. S., W. J. Fulkerson, R. Lawrie, and I. M. Barchia. 2009. Difference in yield and persistence among perennial forages used by the dairy industry under optimum and deficit irrigation. Crop Pasture Sci. 60:1071-1087.
  29. Ofori, F. and W. R. Stern. 1987. Cereal-legume intercropping systems. Adv. Agron. 41:41-90.
  30. Pembleton, K. G., R. P. Rawnsley, and D. J. Donaghy. 2011. Yield and water-use efficiency of contrasting lucerne genotypes grown in a cool temperate environment. Crop Pasture Sci. 62:610-623.
  31. Robertson, M. J., W. Sakala, T. Benson, and Z. Shamudzaria. 2005. Simulating response of maize to previous velvet bean (Mucuna pruriens) crop and nitrogen fertilizer in Malawi. Field Crops Res. 91:91-105.
  32. Stockdale, C. R. 1983. Irrigated pasture productivity and its variability in the Shepparton Region of northern Victoria. Aust. J. Exp. Agric. Anim. Husb. 23:131-139.
  33. Zadoks, J. C., T. T. Chang, and C. F. Konzak. 1974. A decimal code for growth stages of cereals. Weed Res. 14:415-421.

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