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

Ojeda, Jonathan J.; Pembleton, KG; Islam, M. Khyrul; Agnusdei, M. G.; García, S. C.

doi:10.1016/j.agsy.2015.12.005

Cited by 24 publications

(19 citation statements)

References 34 publications

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“…were created according to practices used in the field and are reported in detail in the supplementary information for switchgrass (Table S3) and Miscanthus (Table S4). The harvesting rules were set to remove the biomass up to 0.03 m (Ojeda et al, 2016). When the dates of management interventions were not available, local average dates for the application of these practices were used.…”

Section: Andmentioning

confidence: 99%

“…The original APSIM Lucerne module was developed and extensively tested in many environments for its ability to predict the phenology and DM yield of lucerne (Robertson et al, 2002;Dolling et al, 2005;Chen et al, 2008;Pembleton et al, 2011;Moot et al, 2015;Ojeda et al, 2016). However, in its original format with thermal parameters for a C3 species, the module is not able to adequately simulate switchgrass DM yield.…”

Section: Switchgrassmentioning

confidence: 99%

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Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

et al. 2016

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TITLE Evaluation of Agricultural Production Systems Simulator (APSIM) as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments RUNNING TITLE APSIM simulating perennial bioenergy crops ABSTRACT Simulation models for perennial energy crops such as switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus x giganteus) can be useful tools to design management strategies for biomass productivity improvement in US environments. The Agricultural Production Systems Simulator (APSIM) is a biophysical model with the potential to simulate the growth of perennial crops. APSIM crop modules do not exist for switchgrass and Miscanthus, Accepted ArticleThis article is protected by copyright. All rights reserved.however, re-parameterization of existing APSIM modules could be used to simulate the growth of these perennials. Our aim was to evaluate the ability of APSIM to predict the dry matter (DM) yield of switchgrass and Miscanthus at several US locations. The Lucerne (for switchgrass) and Sugarcane (for Miscanthus) APSIM modules were calibrated using data from four locations in Indiana. A sensitivity analysis informed the relative impact of changes in plant and soil parameters of APSIM Lucerne and APSIM Sugarcane modules. An independent dataset of switchgrass and Miscanthus DM yields from several US environments was used to validate these re-parameterized APSIM modules. The re-parameterized modules simulated DM yields of switchgrass (0.95 for CCC [concordance correlation coefficient] and 0 for SB [bias of the simulation from the measurement]) and Miscanthus (0.65 and 0 % for CCC and SB, respectively) accurately at most locations with the exception of switchgrass at southern US sites (0.01 for CCC and 2 % for SB). Therefore, the APSIM model is a promising tool for simulating DM yields for switchgrass and Miscanthus while accounting for environmental variability. Given our study was strictly based on APSIM calibrations at Indiana locations, additional research using more extensive calibration data may enhance APSIM robustness.

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Section: Andmentioning

confidence: 99%

Section: Switchgrassmentioning

confidence: 99%

Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

et al. 2016

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“…The Argentinian Pampas is an important livestock production region (Solbrig and Viglizzo, 1999), in which animal feed is predominantly based on forage crops sequences (FCS, i.e. sequences based on annual forage crops for silage, hay or grazing) and perennial pastures (Ojeda et al, 2016). In recent years, the sowing area of forage crops (annual and perennial) has decreased significantly in the face of the advance of grain and oilseed cropping (annual crops like soybean, wheat, barley, and sunflower) in this region.…”

Section: Introductionmentioning

confidence: 99%

Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas

Ojeda

Pembleton

Caviglia

et al. 2018

European Journal of Agronomy

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In recent years, the use of forage crop sequences (FCS) has been increased as a main component into the animal rations of the Argentinian pasture-based livestock systems. However, it is unclear how year-by-year rainfall variability and interactions with soil properties affect FCS dry matter (DM) yield in these environments. Biophysical crop models, such as Agricultural Production Systems Simulator (APSIM), are tools that enable the evaluation of crop yield variability across a wide of environments. The objective of this study was to evaluate the APSIM ability to predict forage DM yield and water productivity (WP) of multiple continuous FCS. Thirteen continuous FCS, including winter and summer crops, were simulated by APSIM during two/three growing seasons in five locations across the Argentinian Pampas. Our modelling approach was based on the simulation of multiple continuous FCS, in which crop DM yields depend on the performance of the previous crop in the same sequence and the final soil variables of the previous crop are the initial conditions for the next crop. Overall, APSIM was able to accurately simulate FCS DM yield (0.93 and 3.2 Mg ha-1 for concordance correlation coefficient [CCC] and root mean square error [RMSE] respectively). On the other hand, the model predictions were better for annual (CCC=0.94; RMSE=0.4 g m-2 mm-1) than for seasonal WP (CCC=0.71; RMSE=1.9 g m-2 mm-1), i.e. at the crop level. The model performance to predict WP was associated with better estimations of the soil water dynamics over the long-term, i.e. at the FCS level, rather than the short-term, i.e. at the crop level. The ability of APSIM to predict WP decreased as seasonal WP values increased, i.e. for low water inputs. For seasonal water inputs, <200 mm, the model tended to under-predict WP, which was directly associated with crop DM yield under-predictions for frequently harvested crops. Even though APSIM showed some weaknesses in predicting seasonal DM yield and WP, i.e. at the crop level, it appears as a potential tool for further research on complementary forage crops based on multiple continuous FCS in the Argentinian livestock systems.

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“…Apart from the study by Ojeda et al (2016) it is rare in literature that lucerne 550 has been tested across such a range of diverse locations. In the present study the inclusion of the two 551 lower-rainfall sites at Quairading and Cootamundra, and both winter and summer rainfall environments, 552 means that a full evaluation of the sites where lucerne is grown has been carried out.…”

Section: Predictions Of Lucerne Growth Rate Patterns Across Sites Andmentioning

confidence: 99%

“…Simulations of each experiment were constructed using the GRAZPLAN pasture and livestock models 317 Moore et al 1997) linked to the APSIM soil water and soil nutrient cycling models 318 (Holzworth et al 2014) Cranbrook sites were taken from the modelling study of Ojeda et al (2016), which also used the APSIM 327 soil water balance model. Local soil data for some sites such as the Tamworth experiments in particular 328 were limited, which introduces uncertainty into model predictions from the outset.…”

Section: Simulation Of the Experimental Data Sets 316mentioning

confidence: 99%

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

et al. 2017

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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

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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

Cited by 24 publications

References 34 publications

Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas

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

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