Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Pogson, Mark; Hastings, Astley; Smith, Pete

doi:10.1016/j.envsoft.2011.10.008

Cited by 41 publications

(28 citation statements)

References 29 publications

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“…In this regard, some studies highlighted the importance of accurate modification of the crop growth parameters to increase the performance of the model (Zeleke et al 2011;Todorovic et al 2009). Several sensitivity analyses have shown the importance of soil water characteristics and available soil water for accurate crop growth and yield simulation under water stress; and advised more attention should be paid to soil water properties (Vanuytrecht et al 2014a;Paredes et al 2014;Pogson et al 2012) and soil types (Singh et al 2013). …”

Section: In-season Biomass Developmentmentioning

confidence: 99%

Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Ahmadi

Mosallaeepour

Kamgar-Haghighi

et al. 2015

Water Resour Manage

100

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The AquaCrop model was used to simulate maize growth and soil water content under full and deficit irrigation managements as 1.2, 1, 0.8, and 0.6 of the potential crop water requirement. Generally, the RMSEs in simulating soil water content in calibration and validation were 0.01-0.039 and 0.012-0.037 m 3 m −3 , respectively, that overall corresponds to 3-14 % error. For the in-season biomass development, the RMSEs in calibration varied between 2.16 and 2.73 Mg ha −1 , while they varied between 1.97 and 5.19 Mg ha −1 in validation for the four irrigation managements. The model showed poor performance for simulating biomass late in the season under deficit irrigation managements. The RMSEs of final grain yield simulation were 0.71 and 1.77 Mg ha −1 that corresponded to 7 and 18 % error in calibration and validation, respectively. Likewise, the RMSEs for simulating the final biomass in calibration and validation were 1.29 and 2.21 Mg ha −1 that equals to 6 and 10 % error, respectively. Results demonstrated that AquaCrop is a useful decision-making tool for investigating deficit irrigations and maize growth in the region. However, in agreement with the findings in earlier studies on AquaCrop, the model showed insufficient accuracy in simulating final grain yield and biomass under moderate to severe water stresses. It is suggested that AquaCrop would benefit of including some calibrating parameters about the root distribution pattern in the soil because it is a water-driven model and highly depends on the accurately simulated water uptake from the soil profile.

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Section: In-season Biomass Developmentmentioning

confidence: 99%

Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Ahmadi

Mosallaeepour

Kamgar-Haghighi

et al. 2015

Water Resour Manage

100

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“…Gridded spatial data are used as parameter inputs and outputs in all kinds of spatial models, including ecological, meteorological and hydrological (Fischer and Wang, 2011;Pogson et al, 2012;Yang et al, 2008). The spatial detail, or resolution, of data affects how well they represent reality, as well as how accurately they can be combined with other spatial data within models to make predictions.…”

Section: Introductionmentioning

confidence: 99%

Effect of spatial data resolution on uncertainty

Pogson

Smith

2015

Environmental Modelling & Software

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The effect that the resolution of spatial data has on uncertainty is important to many areas of research.In order to understand this better, the effect of changing resolution is considered for a range of data.An estimate is presented for how the average uncertainty of each grid value varies with grid size, which is shown to be in good agreement with observed uncertainties. The effect of bilinear interpolation is also investigated and is observed to provide no reduction in uncertainty relative to uninterpolated data. Finally, the effects of combining aggregated spatial data are found to obey standard properties of error propagation, which means that the presented estimate of uncertainty can be used to estimate resolution-related uncertainty in spatial model results, relative to the input data.The study quantitatively demonstrates the important role of the spatial autocorrelation of data in uncertainties associated with the resolution of spatial data.

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“…3). In a similar type of study, Pogson et al (2012) drove a miscanthus growth model with different meteorological and soil data sets, and also found that changes in several input variables, rather than individual ones, caused variations in simulated yields.…”

Section: Multi-scale Analysis At Individual Evaluation Sitesmentioning

confidence: 93%

“…However, most sensitivity analysis methods focus on single parameters rather than groups of covarying values, such as when entire input data sets are replaced (e.g. Pogson et al, 2012). More important than the methods used is the requirement that data independent of model development and calibration be used for evaluation and that statistics are appropriate to output variables of interest.…”

Section: Introductionmentioning

confidence: 98%

Reducing the impact of model scale on simulated, gridded switchgrass yields

Vittorio

Miller

2014

Environmental Modelling & Software

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a b s t r a c tResults of gridded ecosystem simulations of bioenergy crops are used for estimating economic viability, environmental impacts, and potential land use change. Gridded model uncertainty propagates through these uses, thus we propose a simple method for estimating regional, spatial model error from sparse field data. We apply this method to the Agricultural-BioGeochemical Cycles (Agro-BGC) model to examine and reduce the model uncertainty associated with grid scale for simulated switchgrass yields in a 6 latitude Â 5 longitude (w300,000 km 2 ) region covering Illinois, United States of America. Based on three evaluation sites, changes in yield with scale result from complex intra-model interactions driven by a combination of meteorological rather than soil or terrain variables. Spatial bias of the regional mean significantly increases with increasing cell size for 11 of 15 measurement dates. This bias is primarily due to grid scale, thus bias correction of output yield reduces the model uncertainty associated with grid scale. The corresponding Root Mean Squared Error and Bias-Corrected RMSE (RMSE BC ) have effectively negligible trends with inconsistent signs. The range of RMSE BC for 2-year Average Mature August Yield (AMAY) is 267e285 g C m À2 across 3-to 3600-arcsec resolution (w90 mew100 km) with biases from 9 to 61 g C m À2 . AMAY bias significantly increases with increasing cell size. Spatial bias of the regional mean is relatively consistent for resolutions 1200 arcsec (w33 km) (AMAY bias <3%), and larger AMAY biases (4e13%) at coarse resolutions indicate poorly characterized spatial heterogeneity. Including the 68% confidence interval around bias-corrected values, AMAY ranges from 0 to 1116 g C m À2 across a 150-arsec grid, which is similar to the range reported for 24 eastern United States field sites. Spatial bias of the regional mean yield can vary across grid resolution by as much as 31% of the observed regional mean and can dramatically affect calculations dependent on the resolution of the estimate. We conclude that grid scale profoundly affects model accuracy such that regional studies must match evaluation and simulation scales and should utilize multi-scale analyses to determine robustness of results.

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Sensitivity of crop model predictions to entire meteorological and soil input datasets highlights vulnerability to drought

Cited by 41 publications

References 29 publications

Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements

Effect of spatial data resolution on uncertainty

Reducing the impact of model scale on simulated, gridded switchgrass yields

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