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

Abstract

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 modern times. 34Therefore 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. 46Validation 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

Evaluation of morphological and production characteristics and nutritive value of 47 lucerne cultivars/lines in temperate Australia

“…Altinok & Karakaya, 2002), and plant height was highly correlated with herbage yield, which revealed that Haymaster 7, WL925HQ, Cropper 9.5, AV1001, Haymaster 9, SARDI 10 SERIES 2, SF 714QL, Titan 7, and Titan 9 were highly winter-active cultivars with greater rates of regrowth in winter. Winter-active species/cultivars are desirable for filling the feed gap of grazing systems over the winter months in temperate Australia (Smith et al, 2017).…”

Simulating the population dynamics of barley grass (Hordeum spp.) and impacts of weed management strategies in a southern Australian lucerne (Medicago sativa) pasture

“…To generate impacts on barley grass survival and seed production due to competition and climate in each simulated year, data for the lucerne-barley grass pasture were produced in GRASSGRO (Moore et al 1997) using 'lucerne' and 'annual grassearly' species settings, historical climate data , and default soil settings for Wagga Wagga. This model has some limitations in simulating interspecies competition (Donnelly et al 2002) and plant death due to stressors over time (Smith et al 2017)weaknesses common to all crop and pasture biophysical models (Smith et al 2017). However, GRASSGRO was considered the most suitable method for modelling species competition within the barley grass model because GRASSGRO is a broadly accepted model, has the capability to simulate basic species competition, and also takes into account the significant and variable impacts of climate/soil moisture on plant growth over time (Clark et al 2000).…”

“…This model has some limitations in simulating interspecies competition (Donnelly et al . 2002) and plant death due to stressors over time (Smith et al . 2017) – weaknesses common to all crop and pasture biophysical models (Smith et al .…”

“…2002) and plant death due to stressors over time (Smith et al . 2017) – weaknesses common to all crop and pasture biophysical models (Smith et al . 2017).…”

“…CS) was introduced as a stochastic element into the barley grass model (Moore et al . 1997; Smith et al . 2017), enabling adjustment of seed production based on year type.…”