APSIM Next Generation: Overcoming challenges in modernising a farming systems model

Holzworth, Dean; Huth, Neil; Fainges, Justin; Brown, Hamish E.; Zurcher, Eric J.; Cichota, Rogerio; Verrall, S.; Herrmann, Neville I.; Zheng, Bangyou; Snow, Val

doi:10.1016/j.envsoft.2018.02.002

Cited by 224 publications

(132 citation statements)

References 51 publications

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“…Similar findings regarding lack of IT expertise we pointed out by (Reinmuth and Dabbert, 2017) for instance. Some of these issues will be hopefully handled in the APSIM Next Generation as outlined by (Holzworth et. al., 2018).…”

Section: Resultsmentioning

confidence: 99%

Data Pre-processing for Agricultural Simulations

Jarolímek

Pavlík

Kholova

et al. 2019

AOL

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The process of agricultural simulation using APSIM requires input meteorological data to be prepared in a specific format and the simulation setting file to be ready before the simulation processing starts. Because of possible time savings when conducting large number of simulations at once, it is preferable to create all the input and settings files for all the simulations beforehand and process the simulations in batches as large as possible. This article specifically deals with the data acquisition, transformation and preparation process. It also outlines initial testing and computing time estimations and discusses scheduling, parallel processing and other possible simulation optimization methods..

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Section: Resultsmentioning

confidence: 99%

Data Pre-processing for Agricultural Simulations

Jarolímek

Pavlík

Kholova

et al. 2019

AOL

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“…The Agricultural Production Systems Simulator (APSIM 7.7) was internationally recognized as a robust analysis tool of agricultural systems (Holzworth et al., 2018). APSIM simulates wheat development, biomass growth, water use, and grain yield in response to temperature, photoperiod, solar radiation, soil water, and N at a daily time step (He et al., 2017b; Wang et al., 2002a).…”

Section: Methodsmentioning

confidence: 99%

Modeling the impacts of climate, soil, and cultivar on optimal irrigation amount of winter wheat in the North China Plain

et al. 2020

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Improving winter wheat (Triticum aestivum L.) productivity and water use efficiency (WUE) in the North China Plain (NCP) is critical to maintain grain security and ease the water shortage in this region. This study was conducted to examine the optimal irrigation amount (OI) for winter wheat across the NCP and analyze its responses to the climate, soil, and cultivar. We applied a novel approach with the Agricultural Production Systems sIMulator (APSIM) during 1961-2010 to estimate the OI by considering the trade-off between yield and WUE. Our results suggested that the optimum irrigation amount determined by climate (OI c ) ranged from 3-342 mm for simulation experiments considering climate impacts alone. When soil conditions were additionally considered, the optimal irrigation amount (OI cs ) showed an average decrease of 45 mm across 19 sites in the northern NCP but an average increase of 42 mm across 29 sites in the southern NCP. When all three factors were included, the estimated optimal irrigation amount (OI csc ) fell within the range of 3-286 mm across the NCP, lower than OI c and OI cs . Climate, soil, and cultivar explained 54, 27, and 19% of the spatial variation in the optimal irrigation amount, respectively. Further analysis indicated relatively higher optimal irrigation amounts for late-maturing cultivars, but their inter-annual variations and decadal changing rates were lower relative to the early-and medium-maturing cultivars. Our study demonstrated the incorporation of the genotypes and environmental factors, and recommended the optimal irrigation amount of 3-286 mm for winter wheat in the NCP.Abbreviations: APSIM, Agricultural Production Systems sIMulator; ET, evapotranspiration; NCP, North China Plain; OI, optimal irrigation amount; OI c , optimal irrigation amount determined by climate; OI cs , optimal irrigation amount considering climate and soil conditions; OI csc , optimal irrigation amount influenced by climate, soil, and cultivar; OI cse , estimated optimal irrigation amount of early-maturing variety Zhengmai 9023; OI csl , estimated optimal irrigation amount of late-maturing variety Lumai 21; OI csm , estimated optimal irrigation amount of medium-maturing variety Gaoyou 503; PAWC, plant available water content; PCA, principal component analysis; WUE, water use efficiency.

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“…Basic soil characteristics are, however, essential for supplying pedotransfer functions that can predict, for example, soil hydraulic properties to be used for modelling soil-water regimes (Bouma, 1989;Van Looy et al, 2017). SWAP simulation models have been applied and tested for decades, for example IBSNAT in the USA (Jones et al, 2003;White et al, 2013), SWAP in the Netherlands (Kroes et al,2015(Kroes et al, ,2017 and APSIM in Australia (Holsworth et al, 2014(Holsworth et al, , 2018. Recent examples of these modelling approaches show that procedures are also well-tested and accurate, even when applied to developing countries (e.g.…”

Section: Dynamic Simulation Modellingmentioning

confidence: 99%

The challenge for the soil science community to contribute to the implementation of the UN Sustainable Development Goals

Bouma¹,

Montanarella

Evanylo

2019

Soil Use and Management

101

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Seventeen Sustainable Development Goals (SDGs) were adopted by 193 Governments at the General Assembly of the United Nations in 2015 for achievement by 2030. These SDGs present a roadmap to a sustainable future and a challenge to the science community. To guide activities and check progress, targets and indicators have been and are still being defined. The soil science community has published documents that describe the primary importance of soil for SDGs addressing hunger, water quality, climate mitigation and biodiversity preservation, and secondary relevance of soil for addressing several other SDGs. Soil scientists only marginally participated in the SDG discussions and are currently only peripherally engaged in discussions on targets or indicators. Agreement on several soil‐related indicators has still not been achieved. Involvement of soil scientists in SDG‐based studies is desirable for both developing solutions and increasing the visibility of the soil profession. Inputs into policy decisions should be improved as SDG committee members are appointed by Governments. Possible contributions of soil science in defining indicators for the SDGs are explored in this paper. We advocate the pragmatic use of soil–water–atmosphere–plant simulation models and available soil surveys and soil databases where “representative” soil profiles for mapping units (genetically defined genoforms) are functionally expressed in terms of several phenoforms reflecting effects of different types of soil use and management that strongly affect functionality.

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APSIM Next Generation: Overcoming challenges in modernising a farming systems model

Cited by 224 publications

References 51 publications

Data Pre-processing for Agricultural Simulations

Data Pre-processing for Agricultural Simulations

Modeling the impacts of climate, soil, and cultivar on optimal irrigation amount of winter wheat in the North China Plain

The challenge for the soil science community to contribute to the implementation of the UN Sustainable Development Goals

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