Register      Login
Soil Research Soil Research Society
Soil, land care and environmental research
RESEARCH ARTICLE

Sensitivity analysis of soil parameters in the Agricultural Production Systems sIMulator (APSIM)

Iris Vogeler https://orcid.org/0000-0003-2512-7668 A * , Joanna Sharp B , Rogerio Cichota B and Linda Lilburne C
+ Author Affiliations
- Author Affiliations

A Department of Agroecology, Aarhus University, Blichers Alle 20, Tjele 8830, Denmark.

B The New Zealand Institute for Plant and Food Research Limited, Lincoln, New Zealand.

C Manaaki Whenua – Landcare Research, Lincoln, New Zealand.

* Correspondence to: iris.vogeler@agro.au.dk

Handling Editor: Gavan McGrath

Soil Research 61(2) 176-186 https://doi.org/10.1071/SR22110
Submitted: 12 May 2022  Accepted: 2 September 2022   Published: 20 September 2022

© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

Context: The performance of process-based agroecosystem simulation models is highly sensitive to the numerous input parameters, many associated with high variability and uncertainty.

Aims: Our aims were to: (1) test the accuracy of the Agricultural Production Systems sIMulator (APSIM) model regarding the prediction of soil water storage and movement in a pasture system with a free draining pumice soil based on site-specific soil hydraulic properties; (2) identify sensitive soil hydraulic properties on model outputs; and (3) identify the influence of uncertainty in the description of soil properties on various model outputs.

Methods: We carried out a sensitivity analysis (SA) to identify sensitive soil hydraulic parameters. We set up APSIM to simulate a pasture system on a free-draining pumice soil in New Zealand. The model was first established with site-specific soil hydraulic properties and outputs were compared with measured soil moisture status and drainage. Next, the model’s sensitivity to the soil hydraulic parameters was assessed for various outputs linked to production and environmental outcomes.

Key results: Varying the various hydraulic parameters affected soil moisture status, but it had generally little effect on drainage, N leaching, and pasture production in this system.

Conclusions: The results suggest that for well-drained soils in a high precipitation zone with no water limitation, the model has low sensitivity to soil hydraulic parameters. Further analysis is required for different soils and for drier conditions.

Implications: For well-drained soils and under non-limiting water conditions the use of general data from databases, rather than site specific measurement of hydraulic properties is justified.

Keywords: crop modelling, environmental modelling, nitrate leaching, pasture dry matter production, soil databases, soil hydraulic properties, soil variability.


References

Ajami M, Heidari A, Khormali F, Zeraatpisheh M, Gorji M, Ayoubi S (2020) Spatial variability of rainfed wheat production under the influence of topography and soil properties in loess-derived soils, Northern Iran. International Journal of Plant Production 14, 597–608.
Spatial variability of rainfed wheat production under the influence of topography and soil properties in loess-derived soils, Northern Iran.Crossref | GoogleScholarGoogle Scholar |

Baey C, Didier A, Lemaire S, Maupas F, Cournède P-H (2014) Parametrization of five classical plant growth models applied to sugar beet and comparison of their predictive capacity on root yield and total biomass. Ecological Modelling 290, 11–20.
Parametrization of five classical plant growth models applied to sugar beet and comparison of their predictive capacity on root yield and total biomass.Crossref | GoogleScholarGoogle Scholar |

Barkle GF, Wöhling T, Stenger R, Mertens J, Moorhead B, Wall A, Clague J (2011) Automated equilibrium tension lysimeters for measuring water fluxes through a layered, volcanic vadose profile in New Zealand. Vadose Zone Journal 10, 747–759.
Automated equilibrium tension lysimeters for measuring water fluxes through a layered, volcanic vadose profile in New Zealand.Crossref | GoogleScholarGoogle Scholar |

Campbell K, McKay MD, Williams BJ (2006) Sensitivity analysis when model outputs are functions. Reliability Engineering & System Safety 91, 1468–1472.
Sensitivity analysis when model outputs are functions.Crossref | GoogleScholarGoogle Scholar |

Campolongo F, Cariboni J, Saltelli A (2007) An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software 22, 1509–1518.
An effective screening design for sensitivity analysis of large models.Crossref | GoogleScholarGoogle Scholar |

Cariboni J, Gatelli D, Liska R, Saltelli A (2007) The role of sensitivity analysis in ecological modelling. Ecological Modelling 203, 167–182.
The role of sensitivity analysis in ecological modelling.Crossref | GoogleScholarGoogle Scholar |

Casadebaig P, Zheng B, Chapman S, Huth N, Faivre R, Chenu K (2016) Assessment of the potential impacts of wheat plant traits across environments by combining crop modeling and global sensitivity analysis. PLoS ONE 11, e0146385
Assessment of the potential impacts of wheat plant traits across environments by combining crop modeling and global sensitivity analysis.Crossref | GoogleScholarGoogle Scholar |

Chen C, Lawes R, Fletcher A, Oliver Y, Robertson M, Bell M, Wang E (2016) How well can APSIM simulate nitrogen uptake and nitrogen fixation of legume crops? Field Crops Research 187, 35–48.
How well can APSIM simulate nitrogen uptake and nitrogen fixation of legume crops?Crossref | GoogleScholarGoogle Scholar |

Cichota R, Vogeler I, Snow VO, Webb TH (2013) Ensemble pedotransfer functions to derive hydraulic properties for New Zealand soils. Soil Research 51, 94–111.
Ensemble pedotransfer functions to derive hydraulic properties for New Zealand soils.Crossref | GoogleScholarGoogle Scholar |

Corre MD, Schnabel RR, Stout WL (2002) Spatial and seasonal variation of gross nitrogen transformations and microbial biomass in a northeastern US grassland. Soil Biology and Biochemistry 34, 445–457.
Spatial and seasonal variation of gross nitrogen transformations and microbial biomass in a northeastern US grassland.Crossref | GoogleScholarGoogle Scholar |

Dann RL, Close ME, Lee R, Pang L (2006) Impact of data quality and model complexity on prediction of pesticide leaching. Journal of Environmental Quality 35, 628–640.
Impact of data quality and model complexity on prediction of pesticide leaching.Crossref | GoogleScholarGoogle Scholar |

Dokoohaki H, Miguez FE, Archontoulis S, Laird D (2018) Use of inverse modelling and Bayesian optimization for investigating the effect of biochar on soil hydrological properties. Agricultural Water Management 208, 268–274.
Use of inverse modelling and Bayesian optimization for investigating the effect of biochar on soil hydrological properties.Crossref | GoogleScholarGoogle Scholar |

Elkateb T, Chalaturnyk R, Robertson PK (2003) An overview of soil heterogeneity: quantification and implications on geotechnical field problems. Canadian Geotechnical Journal 40, 1–15.
An overview of soil heterogeneity: quantification and implications on geotechnical field problems.Crossref | GoogleScholarGoogle Scholar |

Engelhardt IC, Niklaus PA, Bizouard F, Breuil M-C, Rouard N, Deau F, Philippot L, Barnard RL (2021) Precipitation patterns and N availability alter plant-soil microbial C and N dynamics. Plant and Soil 466, 151–163.
Precipitation patterns and N availability alter plant-soil microbial C and N dynamics.Crossref | GoogleScholarGoogle Scholar |

Falco N, Wainwright HM, Dafflon B, Ulrich C, Soom F, Peterson JE, Brown JB, Schaettle KB, Williamson M, Cothren JD, Ham RG, McEntire JA, Hubbard SS (2021) Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery. Scientific Reports 11, 7046
Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery.Crossref | GoogleScholarGoogle Scholar |

Graf A, Herbst M, Weihermüller L, Huisman JA, Prolingheuer N, Bornemann L, Vereecken H (2012) Analyzing spatiotemporal variability of heterotrophic soil respiration at the field scale using orthogonal functions. Geoderma 181-182, 91–101.
Analyzing spatiotemporal variability of heterotrophic soil respiration at the field scale using orthogonal functions.Crossref | GoogleScholarGoogle Scholar |

Hao S, Ryu D, Western A, Perry E, Bogena H, Franssen HJH (2021) Performance of a wheat yield prediction model and factors influencing the performance: a review and meta-analysis. Agricultural Systems 194, 103278
Performance of a wheat yield prediction model and factors influencing the performance: a review and meta-analysis.Crossref | GoogleScholarGoogle Scholar |

Holzworth DP, Huth NI, deVoil PG, Zurcher EJ, Herrmann NI, McLean G, Chenu K, van Oosterom EJ, Snow V, Murphy C, Moore AD, Brown H, Whish JPM, Verrall S, Fainges J, Bell LW, Peake AS, Poulton PL, Hochman Z, Thorburn PJ, Gaydon DS, Dalgliesh NP, Rodriguez D, Cox H, Chapman S, Doherty A, Teixeira E, Sharp J, Cichota R, Vogeler I, Li FY, Wang E, Hammer GL, Robertson MJ, Dimes JP, Whitbread AM, Hunt J, van Rees H, McClelland T, Carberry PS, Hargreaves JNG, MacLeod N, McDonald C, Harsdorf J, Wedgwood S, Keating BA (2014) APSIM – evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327–350.
APSIM – evolution towards a new generation of agricultural systems simulation.Crossref | GoogleScholarGoogle Scholar |

Homma T, Saltelli A (1996) Importance measures in global sensitivity analysis of nonlinear models. Reliability Engineering & System Safety 52, 1–17.
Importance measures in global sensitivity analysis of nonlinear models.Crossref | GoogleScholarGoogle Scholar |

Huth NI, Bristow KL, Verburg K (2012) SWIM3: model use, calibration, and validation. Transactions of the ASABE 55, 1303–1313.
SWIM3: model use, calibration, and validation.Crossref | GoogleScholarGoogle Scholar |

Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ (2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267–288.
An overview of APSIM, a model designed for farming systems simulation.Crossref | GoogleScholarGoogle Scholar |

Khaembah EN, Brown HE, Zyskowski R, Chakwizira E, de Ruiter JM, Teixeira EI (2017) Development of a fodder beet potential yield model in the next generation APSIM. Agricultural Systems 158, 23–38.
Development of a fodder beet potential yield model in the next generation APSIM.Crossref | GoogleScholarGoogle Scholar |

Launay M, Guérif M (2003) Ability for a model to predict crop production variability at the regional scale: an evaluation for sugar beet. Agronomie 23, 135–146.
Ability for a model to predict crop production variability at the regional scale: an evaluation for sugar beet.Crossref | GoogleScholarGoogle Scholar |

Li C, Mosier A, Wassmann R, Cai Z, Zheng X, Huang Y, Tsuruta H, Boonjawat J, Lantin R (2004) Modeling greenhouse gas emissions from rice-based production systems: sensitivity and upscaling. Global Biogeochemical Cycles 18, GB1043
Modeling greenhouse gas emissions from rice-based production systems: sensitivity and upscaling.Crossref | GoogleScholarGoogle Scholar |

Li FY, Snow VO, Holzworth DP (2011) Modelling the seasonal and geographical pattern of pasture production in New Zealand. New Zealand Journal of Agricultural Research 54, 331–352.
Modelling the seasonal and geographical pattern of pasture production in New Zealand.Crossref | GoogleScholarGoogle Scholar |

Liang H, Gao S, Hu K (2020) Global sensitivity and uncertainty analysis of the dynamic simulation of crop N uptake by using various N dilution curve approaches. European Journal of Agronomy 116, 126044
Global sensitivity and uncertainty analysis of the dynamic simulation of crop N uptake by using various N dilution curve approaches.Crossref | GoogleScholarGoogle Scholar |

Lilburne LR, Hewitt AE, Webb TW (2012) Soil and informatics science combine to develop S-map: a new generation soil information system for New Zealand. Geoderma 170, 232–238.
Soil and informatics science combine to develop S-map: a new generation soil information system for New Zealand.Crossref | GoogleScholarGoogle Scholar |

Liu J, Liu Z, Zhu A-X, Shen F, Lei Q, Duan Z (2019) Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions. Science of The Total Environment 651, 953–968.
Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions.Crossref | GoogleScholarGoogle Scholar |

Malone RW, Yagow G, Baffaut C, Gitau MW, Qi Z, Amatya DM, Parajuli PB, Bonta JV, Green TR (2015) Parameterization guidelines and considerations for hydrologic models. Transactions of the ASABE 58, 1681–1703.
Parameterization guidelines and considerations for hydrologic models.Crossref | GoogleScholarGoogle Scholar |

Massmann C, Wagener T, Holzmann H (2014) A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales. Environmental Modelling & Software 51, 190–194.
A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales.Crossref | GoogleScholarGoogle Scholar |

Moreau P, Viaud V, Parnaudeau V, Salmon-Monviola J, Durand P (2013) An approach for global sensitivity analysis of a complex environmental model to spatial inputs and parameters: a case study of an agro-hydrological model. Environmental Modelling & Software 47, 74–87.
An approach for global sensitivity analysis of a complex environmental model to spatial inputs and parameters: a case study of an agro-hydrological model.Crossref | GoogleScholarGoogle Scholar |

Ogle SM, Breidt FJ, Easter M, Williams S, Killian K, Paustian K (2010) Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process-based model. Global Change Biology 16, 810–822.

Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T (2016) Sensitivity analysis of environmental models: a systematic review with practical workflow. Environmental Modelling & Software 79, 214–232.
Sensitivity analysis of environmental models: a systematic review with practical workflow.Crossref | GoogleScholarGoogle Scholar |

Probert ME, Dimes JP, Keating BA, Dalal RC, Strong WM (1998) APSIM’s water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems. Agricultural Systems 56, 1–28.
APSIM’s water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems.Crossref | GoogleScholarGoogle Scholar |

Qin F, Zhao Y, Shi X, Xu S, Yu D (2016) Sensitivity and uncertainty analysis for the DeNitrification–DeComposition model, a case study of modeling soil organic carbon dynamics at a long-term observation site with a rice–bean rotation. Computers and Electronics in Agriculture 124, 263–272.
Sensitivity and uncertainty analysis for the DeNitrification–DeComposition model, a case study of modeling soil organic carbon dynamics at a long-term observation site with a rice–bean rotation.Crossref | GoogleScholarGoogle Scholar |

Rahmati M, Weihermüller L, Vanderborght J, et al. (2018) Development and analysis of the Soil Water Infiltration Global database. Earth System Science Data 10, 1237–1263.
Development and analysis of the Soil Water Infiltration Global database.Crossref | GoogleScholarGoogle Scholar |

Ritter A, Hupet F, Muñoz-Carpena R, Lambot S, Vanclooster M (2003) Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods. Agricultural Water Management 59, 77–96.
Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods.Crossref | GoogleScholarGoogle Scholar |

Robert CP (1995) Simulation of truncated normal variables. Statistics and Computing 5, 121–125.
Simulation of truncated normal variables.Crossref | GoogleScholarGoogle Scholar |

Ross PJ, Smettem KRJ (1993) Describing soil hydraulic properties with sums of simple functions. Soil Science Society American Journal 57, 26–29.

Saddique Q, Ji J, Ajaz A, Jiatun X, Yufeng Z, He J, Cai H (2019) ‘Performance comparison of the APSIM and CERES-Wheat models in Guanzhong Plain, China.’ (American Society of Agricultural and Biological Engineers: Boston, Massachusetts)

Saltelli A, Annoni P (2010) How to avoid a perfunctory sensitivity analysis. Environmental Modelling & Software 25, 1508–1517.
How to avoid a perfunctory sensitivity analysis.Crossref | GoogleScholarGoogle Scholar |

Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) ‘Global sensitivity analysis: the primer.’ (John Wiley & Sons)

Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications 181, 259–270.
Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index.Crossref | GoogleScholarGoogle Scholar |

Saltelli A, Aleksankina K, Becker W, Fennell P, Ferretti F, Holst N, Li S, Wu Q (2019) Why so many published sensitivity analyses are false: a systematic review of sensitivity analysis practices. Environmental Modelling & Software 114, 29–39.
Why so many published sensitivity analyses are false: a systematic review of sensitivity analysis practices.Crossref | GoogleScholarGoogle Scholar |

Sexton J, Everingham YL, Inman-Bamber G (2017) A global sensitivity analysis of cultivar trait parameters in a sugarcane growth model for contrasting production environments in Queensland, Australia. European Journal of Agronomy 88, 96–105.
A global sensitivity analysis of cultivar trait parameters in a sugarcane growth model for contrasting production environments in Queensland, Australia.Crossref | GoogleScholarGoogle Scholar |

Sobol IM (1993) Sensitivity estimates for nonlinear mathematical models. Mathematical modelling and computational experiments 1, 407–414.

Stenger R, Wöhling T, Barkle GF, Wall A (2007) Relationship between dielectric permittivity and water content for vadose zone materials of volcanic origin. Australian Journal of Soil Research 45, 299–309.
Relationship between dielectric permittivity and water content for vadose zone materials of volcanic origin.Crossref | GoogleScholarGoogle Scholar |

Thorp KR, DeJonge KC, Marek GW, Evett SR (2020) Comparison of evapotranspiration methods in the DSSAT Cropping System Model: I. Global sensitivity analysis. Computers and Electronics in Agriculture 177, 105658
Comparison of evapotranspiration methods in the DSSAT Cropping System Model: I. Global sensitivity analysis.Crossref | GoogleScholarGoogle Scholar |

Vanuytrecht E, Raes D, Willems P (2014) Global sensitivity analysis of yield output from the water productivity model. Environmental Modelling & Software 51, 323–332.
Global sensitivity analysis of yield output from the water productivity model.Crossref | GoogleScholarGoogle Scholar |

Varella H, Guérif M, Buis S (2010) Global sensitivity analysis measures the quality of parameter estimation: the case of soil parameters and a crop model. Environmental Modelling & Software 25, 310–319.
Global sensitivity analysis measures the quality of parameter estimation: the case of soil parameters and a crop model.Crossref | GoogleScholarGoogle Scholar |

Varella H, Buis S, Launay M, Guérif M (2012) Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined. Agricultural Sciences 3, 949–961.
Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined.Crossref | GoogleScholarGoogle Scholar |

Vereecken H, Maes J, Feyen J (1990) Estimating unsaturated hydraulic conductivity from easily measured soil properties. Soil Science 149, 1–12.
Estimating unsaturated hydraulic conductivity from easily measured soil properties.Crossref | GoogleScholarGoogle Scholar |

Vogeler I, Cichota R (2018) Effect of variability in soil properties plus model complexity on predicting topsoil water content and nitrous oxide emissions. Soil Research 56, 810–819.
Effect of variability in soil properties plus model complexity on predicting topsoil water content and nitrous oxide emissions.Crossref | GoogleScholarGoogle Scholar |

Vogeler I, Cichota R, Snow V (2013) Identification and testing of early indicators for N leaching from urine patches. Journal of Environmental Management 130, 55–63.
Identification and testing of early indicators for N leaching from urine patches.Crossref | GoogleScholarGoogle Scholar |

Vogeler I, Carrick S, Lilburne L, Cichota R, Pollacco J, Fernández-Gálvez J (2021) How important is the description of soil unsaturated hydraulic conductivity values for simulating soil saturation level, drainage and pasture yield? Journal of Hydrology 598, 126257
How important is the description of soil unsaturated hydraulic conductivity values for simulating soil saturation level, drainage and pasture yield?Crossref | GoogleScholarGoogle Scholar |

Vogeler I, Lilburne L, Webb T, Cichota R, Sharp J, Carrick S, Brown H, Snow V (2022) S-map parameters for APSIM. MethodsX 9, 101632
S-map parameters for APSIM.Crossref | GoogleScholarGoogle Scholar |

Wang J, Bogena H, Süß T, Graf A, Weuthen A, Brüggemann N (2021) Investigating the controls on greenhouse gas emission in the riparian zone of a small headwater catchment using an automated monitoring system. Vadose Zone Journal 20, e20149
Investigating the controls on greenhouse gas emission in the riparian zone of a small headwater catchment using an automated monitoring system.Crossref | GoogleScholarGoogle Scholar |

Wilde RH (2003) ‘Manual for national soils database.’ (Landcare Research: Palmerston North, New Zealand)

Wöhling T, Vrugt JA (2011) Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data. Water Resources Research 47, W04510
Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data.Crossref | GoogleScholarGoogle Scholar |

Wöhling T, Schütze N, Heinrich B, Šimůnek J, Barkle GF (2009) Three-dimensional modeling of multiple automated equilibrium tension Lysimeters to Measure Vadose zone fluxes. Vadose Zone Journal 8, 1051–1063.
Three-dimensional modeling of multiple automated equilibrium tension Lysimeters to Measure Vadose zone fluxes.Crossref | GoogleScholarGoogle Scholar |

Wu R, Lawes R, Oliver Y, Fletcher A, Chen C (2019) How well do we need to estimate plant-available water capacity to simulate water-limited yield potential? Agricultural Water Management 212, 441–447.
How well do we need to estimate plant-available water capacity to simulate water-limited yield potential?Crossref | GoogleScholarGoogle Scholar |

Xu X, Sun C, Huang G, Mohanty BP (2016) Global sensitivity analysis and calibration of parameters for a physically-based agro-hydrological model. Environmental Modelling & Software 83, 88–102.
Global sensitivity analysis and calibration of parameters for a physically-based agro-hydrological model.Crossref | GoogleScholarGoogle Scholar |