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Evaluation of the agricultural production systems simulator simulating Lucerne and annual ryegrass dry matter yield in the Argentine Pampas and south-eastern Australia

Author

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  • Ojeda, J.J.
  • Pembleton, K.G.
  • Islam, M.R.
  • Agnusdei, M.G.
  • Garcia, S.C.

Abstract

Modelling plant growth provides a tool for evaluating interactions between environment and management of forage crops for pasture-based livestock systems. Consequently, biophysical and farm systems models are becoming important tools for studying production systems that are based on forage crops. The Agricultural Production Systems Simulator (APSIM) is a model with the potential to compare the growth of annual forage crops and perennial pastures. However, information is limited about how accurately the Lucerne and Weed modules represent the growth and development of forage crops and pastures under different managements, soil types and environments in South America. This study evaluated the capacity of APSIM to simulate the growth rates and predict the dry matter (DM) yield of Lucerne (Medicago sativa L.) and annual ryegrass (Lolium multiflorum Lam.) in contrasting climatic regions of Argentina. In addition, at several Australian locations, DM yields of both crops were simulated to ensure that possible changes to the model not interfere with the robust APSIM performance that was already shown in south-eastern Australia. Initial simulations for Lucerne and ryegrass were made with original Lucerne and Weed modules of APSIM, respectively. Simulated DM yield was then compared with field data collected from the same crops grown in five locations in the Argentine Pampas and seven locations in south-eastern Australia over 5 of years. APSIM predicted DM yield of Lucerne at each harvest with reasonable accuracy [0.59, 0.77 and 0.77 for R2, correlation coefficient and concordance correlation coefficient (CCC), respectively]. However, these statistics improved when the DM yield was analysed by annual accumulation, with values of 0.87, 0.93 and 0.92 for R2, correlation coefficient and CCC, respectively. APSIM, generally, over-predicted DM yield of annual ryegrass at the first harvest. Nonetheless, when the Weed module was modified through changes in phenology and transpiration efficiency, performance improved (values of 0.89, 0.94 and 0.93 for R2, correlation coefficient and CCC, respectively). This study showed that annual DM yield of Lucerne can be successfully modelled by the APSIM Lucerne module without any modifications, using a crop modelling approach. However, successfully modelling of Lucerne DM yield by harvest will require further development of the model. Moreover, modification of model parameters associated with phenology and transpiration was required to enable the Weed module of APSIM simulate growth and yield of annual ryegrass in a range of geographic locations within the Argentine Pampas.

Suggested Citation

  • Ojeda, J.J. & Pembleton, K.G. & Islam, M.R. & Agnusdei, M.G. & Garcia, S.C., 2016. "Evaluation of the agricultural production systems simulator simulating Lucerne and annual ryegrass dry matter yield in the Argentine Pampas and south-eastern Australia," Agricultural Systems, Elsevier, vol. 143(C), pages 61-75.
    • Handle: RePEc:eee:agisys:v:143:y:2016:i:c:p:61-75
    DOI: 10.1016/j.agsy.2015.12.005
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    References listed on IDEAS

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    1. Sinclair, T.R. & Salado-Navarro, L.R. & Salas, Graciela & Purcell, L.C., 2007. "Soybean yields and soil water status in Argentina: Simulation analysis," Agricultural Systems, Elsevier, vol. 94(2), pages 471-477, May.
    2. Piñeiro, Gervasio & Perelman, Susana & Guerschman, Juan P. & Paruelo, José M., 2008. "How to evaluate models: Observed vs. predicted or predicted vs. observed?," Ecological Modelling, Elsevier, vol. 216(3), pages 316-322.
    3. Chapman, D.F. & Kenny, S.N. & Beca, D. & Johnson, I.R., 2008. "Pasture and forage crop systems for non-irrigated dairy farms in southern Australia. 2. Inter-annual variation in forage supply, and business risk," Agricultural Systems, Elsevier, vol. 97(3), pages 126-138, June.
    4. Tedeschi, Luis Orlindo, 2006. "Assessment of the adequacy of mathematical models," Agricultural Systems, Elsevier, vol. 89(2-3), pages 225-247, September.
    5. Chapman, D.F. & Kenny, S.N. & Beca, D. & Johnson, I.R., 2008. "Pasture and forage crop systems for non-irrigated dairy farms in southern Australia. 1. Physical production and economic performance," Agricultural Systems, Elsevier, vol. 97(3), pages 108-125, June.
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    1. Della Nave, Facundo N. & Ojeda, Jonathan J. & Irisarri, J. Gonzalo N. & Pembleton, Keith & Oyarzabal, Mariano & Oesterheld, Martín, 2022. "Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions," Agricultural Systems, Elsevier, vol. 201(C).
    2. Ojeda, Jonathan J. & Volenec, Jeffrey J. & Brouder, Sylvie M. & Caviglia, Octavio P. & Agnusdei, Mónica G., 2018. "Modelling stover and grain yields, and subsurface artificial drainage from long-term corn rotations using APSIM," Agricultural Water Management, Elsevier, vol. 195(C), pages 154-171.
    3. Ojeda, Jonathan J. & Huth, Neil & Holzworth, Dean & Raymundo, Rubí & Zyskowski, Robert F. & Sinton, Sarah M. & Michel, Alexandre J. & Brown, Hamish E., 2021. "Assessing errors during simulation configuration in crop models – A global case study using APSIM-Potato," Ecological Modelling, Elsevier, vol. 458(C).
    4. Baudracco, Javier & Lazzarini, Belén & Rossler, Noelia & Gastaldi, Laura & Jauregui, José & Fariña, Santiago, 2022. "Strategies to double milk production per farm in Argentina: Investment, economics and risk analysis," Agricultural Systems, Elsevier, vol. 197(C).
    5. Yang, Xuan & Jia, Pengfei & Hou, Qingqing & Zhu, Min, 2023. "Quantitative sensitivity of crop productivity and water productivity to precipitation during growth periods in the Agro-Pastoral Ecotone of Shanxi Province, China, based on APSIM," Agricultural Water Management, Elsevier, vol. 283(C).

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