Modelling of lucerne (Medicago sativa L.) for livestock production in diverse environments

Andrew P. Smith, Andrew D. Moore, Suzanne P. Boschma, Richard C. Hayes, Zhongnan Nie, Keith G. Pembleton
2017 Crop and Pasture Science  
29 A number of models exist to predict lucerne (Medicago sativa L.) dry matter production; however most 30 of these models do not adequately represent the ecophysiology of the species to predict daily growth 31 rates across the range of environments in which it is grown. Since it was developed in the late 1990s 32 the GRAZPLAN model has not been updated to reflect modern genotypes and has not been widely 33 validated across the range of climates and farming systems in which lucerne is grown in
more » ... odern times. 34 Therefore the capacity of GRAZPLAN pasture growth model to predict lucerne growth and 35 development was assessed. This was done by re-estimating values for some key parameters based on 36 information in the scientific literature. The improved GRAZPLAN model was also assessed for its 37 capacity to reflect differences in the growth and physiology of lucerne genotypes with different winter 38 activity. Modifications were made to GRAZPLAN to improve its capacity to reflect changes in 39 phenology due to environmental triggers such as short photoperiods, declining low temperatures, 40 defoliation and water stress. Changes were also made to the parameter governing the effect of vapour 41 pressure VPD on the biomass-transpiration ratio and therefore biomass accumulation. Other 42 developments included the representation of root development and partitioning of canopy structure, 43 notably the ratio of leaf to stem dry matter. Data from replicated field experiments across Australia 44 were identified for the purpose of model validation. These data were broadly representative of the range 45 of climate zones, soil types and farming systems in which lucerne is used for livestock grazing. 46 Validation of predicted lucerne growth rates was comprehensive due to the plentiful data. Across a 47 range of climate zones, soils and farming systems there was an overall improvement in the capacity to 48 simulate the pasture dry matter production, with a reduction in the mean prediction error of 0.33 and 49 the root mean square deviation of 9.6 kg/ha/d. Validation of other parts of the model was restricted 50 however as information relating to plant roots, soil water, plant morphology and phenology were 51 limited. This study has highlighted the predictive power, versatility and robust nature of GRAZPLAN 52 to predict the growth, development and nutritive value of perennial species such as lucerne. 53 4
doi:10.1071/cp16176 fatcat:4ooqnyqqbbaqti7hfl2b27l64y