Development of a generic crop model template in the cropping system model APSIM

Wang, E.; Robertson, M. J.; Hammer, Graeme; Carberry, P. S.; Holzworth, Dean; Meinke, Holger; Chapman, Scott C.; Hargreaves, J. N. G.; Huth, N. I.; McLean, Greg

doi:10.1016/s1161-0301(02)00100-4

Cited by 230 publications

(138 citation statements)

References 52 publications

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“…The relationship between lamina length and biomass was continuous across phytomers while the relationship between lamina width and biomass was separated by the ear appearance, suggesting that the specific leaf area is also not continuous among phytomers though this parameter is commonly used in crop modelling as a constant (Fournier and Andrieu, 1998;Wang et al, 2002). To improve the modelling accuracy, leaf specific area dependent on ear appearance should be taken into account in maize modelling.…”

Section: Allometric Relationships Between Organ Morphology and Its Bimentioning

confidence: 99%

“…) is currently used as a key parameter to convert leaf function (biomass accumulation) to leaf morphology (leaf area), the latter being then used to integrate into functional computation (Fournier and Andrieu, 1998;Wang et al, 2002;Yan et al, 2004;Guo et al, 2006). Thus allometric relationships at the organ level are required to develop FSPMs, and consequently to enhance modelling capability.…”

mentioning

confidence: 99%

“…Wang et al, 2002). Recently, fresh matter of individual organs was used to establish the model framework in FSPMs (Yan et al, 2004) and later by application in maize (Guo et al, 2006), showing that the use of fresh weight can simplify the process of seeking relationships between plant morphology and biomass accumulation.…”

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confidence: 99%

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Allometric Relationships of Maize Organ Development under Different Water Regimes and Plant Densities

Song

Birch

Rui

et al. 2015

Plant Production Science

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Section: Allometric Relationships Between Organ Morphology and Its Bimentioning

confidence: 99%

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confidence: 99%

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Allometric Relationships of Maize Organ Development under Different Water Regimes and Plant Densities

Song

Birch

Rui

et al. 2015

Plant Production Science

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“…It has vast applications like decision making, forecasting and risk evaluation for sustainable management (Keating et al, 2003). It can simulate the soil and plant dynamics along with their interactions with managements under wide spectrum of farming operations (Wang et al, 2002). It has been parameterized and validated to explain the impacts of climatic variation on wheat yield and to enhance climate forecasting utilizations for rainfed agriculture.…”

Section: Figure 2 Schematic Diagram Of Soil Water Dynamicsmentioning

confidence: 99%

Modelling Soil Water Dynamics under Rainfed Agriculture to Mitigate Climate Change

Ahmed¹,

Hirani²,

Asif³

et al. 2013

JAS

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The model performance to simulate soil water dynamics was evaluated by comparing the predicted soil water content values with calculated soil water at different phenological stages of wheat and total soil available water using neutron probe. The pre-sowing soil water (mm) in this study varied from 40 to 50 mm in loam and sandy clay soil of Islamabad and Chakwal, respectively. When soil water is >50 mm, its effect on crop establishment is dependent on amount and temporal distribution of rainfall. Plant available soil water seems to be the most important factor if rainfall occurs between sowing and floral initiation period as happened during 2008-09 in the present study. The dynamics of soil water from emergence to maturity represented here as total soil water that remained maximum at earlier plant stages especially when crop roots were established and utilized soil water effectively that resulted in the lowest level of water at maturity which can be due to the evapotranspiration. The results depicted that the soil water distribution pattern mainly depends on soil properties and if sowing methodologies are resilient with available soil water then crop stand will be good and crop uses water much effectively. The results also depicted that when soil water is at drain upper limit (DUL) and the crop sown at proper time along with least soil evaporation, the soil water can be more easily taken up by the plant roots. Furthermore, the simulated soil water by the model was in close agreement with actual data. The validation skill scores like R 2 confirmed the actuality of the model, therefore, dynamic model like Agricultural Production System Simulator (APSIM) could be used to describe the distribution of rainwater into different components like infiltration, runoff and drainage, and it can be used as a decision support tool for accurate management of different cultural operations for sustainable atmosphere-soil-plant (ASP) system.

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“…Once each simulation model (Probert et al 1998;Wang et al 2002;Keating et al 2003) (www.apsim.info) (Johnson et al 2003) (www.wfsat. org) (Berntsen et al 2003(Berntsen et al ,2005(Berntsen et al ,2006Hutchings et al 2007) (www.fasset.dk) (Ascough et al 1999;Ándales et al 2003;Shaffer et al 2004;McMaster et al 2005) Freer et al 1997;Moore et al 1997) (Morrison et al 1986;Thornley & Cannell 1997;Thornley 1998) (Savoie et al 1985;Rotz et al 1999b;Rotz & Coiner 2006) (www.ars.…”

Section: Model Identificationmentioning

confidence: 99%

Modelling pastoral farm agro‐ecosystems: A review

Bryant

Snow

2008

New Zealand Journal of Agricultural Research

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A pastoral farm is a complex agro-ecosystem that produces agricultural outputs such as milk, meat and wool. Detailed consideration of the complexities of agro-ecosystem components, and the interactions between these components, may be best approached with simulation modelling. We reviewed nine simulation models (APSIM, EcoMod, FASSET, GRAZPLAN, GPFARM, Hurley Pasture Model, IFSM, LINCFARM, and WFM) used primarily for research of grazing systems. GrassGro (one of the GRAZPLAN suite of decision support tools), LINC-FARM, and WFM had particular strength in their simulation of animal production and farm management. The strengths of APSIM, EcoMod and FAS-SET lie in their simulation of soil nutrient dynam ics, crop or pasture production and spatial variations in soil properties. The Hurley Pasture Model has particular capability to model nutrient cycling in the presence of climate change. The IFSM includes a comprehensive representation of machinery. GP-FARM can be used to predict forage production of different plant functional groups.We suggest that more emphasis could be placed on including the effects of pests and diseases on pasture production and animal performance, more detailed representation of management practices, inclusion of more mechanistic voluntary feed intake and rumen processes and including the effect of specific genes and gene-by-environment interactions on plant quality and yields, nutrient use and animal performance. The Common Modelling Protocol, used in APSIM and the GRAZPLAN suite, may enable the development of a powerful and flexible pastoral agro-ecosystem model which incorporates newly developed modules of the nature mentioned above.

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Development of a generic crop model template in the cropping system model APSIM

Cited by 230 publications

References 52 publications

Allometric Relationships of Maize Organ Development under Different Water Regimes and Plant Densities

Allometric Relationships of Maize Organ Development under Different Water Regimes and Plant Densities

Modelling Soil Water Dynamics under Rainfed Agriculture to Mitigate Climate Change

Modelling pastoral farm agro‐ecosystems: A review

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