Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters

Zhao, Gang; Bryan, Brett A.; Song, Xiaodong

doi:10.1016/j.ecolmodel.2014.02.003

Cited by 116 publications

(76 citation statements)

References 59 publications

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“…Global sensitivity analysis has become the standard technique used to assess the sensitivity of model outputs to variation in model inputs (Gao et al, in preparation;Saltelli et al, 2008;Song et al, 2012;Zhao et al, 2014). However, land use is a categorical variable and global sensitivity analysis techniques have not yet been developed to assess the impact of uncertainty in categorical allocation (such as land use types), on model outputs.…”

Section: Introductionmentioning

confidence: 98%

Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling

et al. 2015

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Section: Introductionmentioning

confidence: 98%

Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling

et al. 2015

Self Cite

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“…[1][2][3][4][5][6] The most important supramolecular forces are ascribed to conventionalh ydrogen bonds (HBs;e .g.,N H···O, OH···O) [1][2][3][7][8][9][10] and weakH Bs (CH···O,CH···p). [32,33] However, there hasb een al ong-term discussion about the nature of these interactions and the relative roles of electrostatics, [34,35] Pauli repulsion, [36,37] dispersion, [38] and charget ransfer (CT). [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] Intensive structural investigations in the fields of biopolymers and organic materials have revealed that these less common interactions contributec onsiderably to the stabilization of various molecular conformations.…”

Section: Introductionmentioning

confidence: 99%

“…[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] Intensive structural investigations in the fields of biopolymers and organic materials have revealed that these less common interactions contributec onsiderably to the stabilization of various molecular conformations. [32,33] However, there hasb een al ong-term discussion about the nature of these interactions and the relative roles of electrostatics, [34,35] Pauli repulsion, [36,37] dispersion, [38] and charget ransfer (CT). [29,39,40] The electron density of the halogen, chalcogen, or pnicogen atoms covalently bonded to an electron-withdrawing atom or group is usually anisotropic.…”

Section: Introductionmentioning

confidence: 99%

Supramolecular Covalence in Bifurcated Chalcogen Bonding

Bora

Novák

Novotný

et al. 2017

Chemistry A European J

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Supramolecular interactions are generally classified as noncovalent. However, recent studies have demonstrated that many of these interactions are stabilized by a significant covalent component. Herein, for systems of the general structure [MX ] :YX (M=Se or Pt; Y=S, Se, or Te; X=F, Cl, Br, I), featuring bifurcated chalcogen bonding, it is shown that, although electrostatic parameters are useful for estimating the long-range electrostatic component of the interaction, they fail to predict the correct order of binding energies in a series of compounds. Instead, the Lewis basicity of the individual substituents X on the chalcogen atom governs the trends in the binding energies through fine-tuning the covalent character of the chalcogen bond. The effects of substituents on the binding energy and supramolecular electron sharing are consistently identified by an arsenal of theoretical methods, ranging from approaches based on the quantum chemical topology to analytical tools based on the localized molecular orbitals. The chalcogen bonding investigated herein is driven by orbital interactions with significant electron sharing; this can be designated as supramolecular covalence.

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“…The general large area model, proposed by Challinor et al (2004) and adapted by Bergamaschi et al (2013) for corn (GLAM-Maize), is another example. However, most of the methods demand data and parameterization that are not easily available (Zhao et al, 2014), specially for large and heterogeneous areas (Reidsma et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

Monteiro

Assad

Sentelhas

et al. 2017

Pesq. agropec. bras.

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-The objective of this work was to develop and evaluate a method for estimating corn yield using a minimum number of parameters and limited information about crop management. The proposed method estimates potential and attainable yields based on the technological level of the production systems and on relatively simple agrometeorological models. Corn yield was estimated for the crop seasons from 2000/2001 to 2007/2008, considering several locations and regions in Brazil, and was compared with the actual yield data from official surveys. There was a high correlation between the estimated and observed yield (0.76≤R 2 <0.92; p<0.01), with model efficiency (E1') ranging from 0.45 to 0.73; mean relative error (MRE) between -0.9 and 2.4%; and mean absolute error (MAE) of less than 70 kg ha -1 , depending on the technological level adopted. Based on these results, the proposed yield model can be recommended to forecast yields all over the country, contributing to make this process more precise and accurate.Index terms: Zea mays, large-area crop modeling, paremetrization, risk analysis, technological potential yield, yield forecast. Modelagem da produtividade de milho no Brasil em função das condições meteorológicas e do nível tecnológicoResumo -O objetivo deste trabalho foi desenvolver e avaliar um método para estimar a produtividade de milho com uso de um número mínimo de parâmetros e de informações limitadas sobre o manejo da cultura. O método proposto estima rendimentos potenciais e atingíveis com base no nível tecnológico dos sistemas de produção e em modelos agrometeorológicos relativamente simples. A produtividade de milho foi estimada para as safras de 2000/2001 a 2007/2008, tendo-se considerado vários locais e regiões do Brasil, e comparada aos dados de produção reais de levantamentos oficiais. A produtividade estimada apresentou alta correlação com a observada (0,76≤R 2 <0,92; p<0,01), com eficiência do modelo (E1') entre 0,45 e 0,73; erro médio relativo (MRE) entre -0,9 e 2,4%; e erro médio absoluto (MAE) inferior a 70 kg ha -1 , de acordo com o nível tecnológico considerado. Com base nestes resultados, este modelo pode ser recomendado para estimativas de produtividade em todo o País, e contribuir para tornar este processo mais preciso e exato.Termos para indexação: Zea mays, modelagem de cultura em grandes áreas, parametrização, análise de risco, produtividade potencial tecnológica, previsão de safra.

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Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters

Cited by 116 publications

References 59 publications

Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling

Land use mapping error introduces strongly-localised, scale-dependent uncertainty into land use and ecosystem services modelling

Supramolecular Covalence in Bifurcated Chalcogen Bonding

Modeling of corn yield in Brazil as a function of meteorological conditions and technological level

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