Evaluating the Agricultural Production Systems sIMulator (APSIM) wheat module for California

Nicholas Alexander George; Helio de Jesus Pedro Cuamba; Mark E. Lundy; Sarita Jane Bennett

doi:10.1071/CP23046

RESEARCH ARTICLE (Open Access)

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Evaluating the Agricultural Production Systems sIMulator (APSIM) wheat module for California

Nicholas Alexander George

^A ^B ^* , Helio de Jesus Pedro Cuamba ^B ^C , Mark E. Lundy ^A ^D and Sarita Jane Bennett

^B

+ Author Affiliations

- Author Affiliations

^A Department of Plant Sciences, University of California, Davis, CA 95616, USA.

^B School of Molecular and Life Sciences, Curtin University, Bentley, WA 6102, Australia.

^C Faculty of Agronomy and Forest Engineering, University of Eduardo Mondlane, P.O. Box 257, Maputo 3453, Mozambique.

^D Division of Agriculture and Natural Resources, University of California, Davis, CA 95618, USA.

^* Correspondence to: nicholas.george@curtin.edu.au

Handling Editor: Davide Cammarano

Crop & Pasture Science 75, CP23046 https://doi.org/10.1071/CP23046

Submitted: 26 July 2022 Accepted: 7 February 2024 Published: 29 February 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Computer-based crop simulation models are important tools for agricultural research and management. APSIM (Agricultural Production Systems sIMulator) is commonly used around the world but has not been widely validated in North America.

Aims

The objective of this work was to evaluate the reliability of APSIM for simulating wheat production in California, with the aim of providing guidance for future field research aimed at model calibration and validation.

Methods

Environmental and management data from state-wide wheat variety trials of common wheat (Triticum aestivum L.) were used to parameterise the APSIM-Wheat module (ver. 7.10 r4220). Simulated yield and protein data were compared with observed field trial results to test the reliability of APSIM simulations.

Key results

The most reliable simulation of grain yield had a root-mean-square error of 1040 kg/ha and normalised root-mean-square error of 16% relative to actual field data. Preliminary calibration of the model for Californian wheat varieties did not improve simulation accuracy or precision.

Conclusions

The accuracy or precision of the simulations was comparable to that of other tests of the APSIM-Wheat module in environments where it has not been previously calibrated but was considered too low to be reliable. The lack of reliability was due to the poor representation of local Californian wheat genotypes by existing APSIM cultivars, as well as possible lack of precision and accuracy of field data.

Implications

APSIM could be a valuable tool for wheat research and management in California; however, further research is needed to generate suitable field data for model calibration and validation.

Keywords: abiotic stress, agronomy, breeding, cereals, cropping systems, farming systems, mediterranean environments.

References

Ahmed M, Akram MN, Asim M, Aslam M, Hassan F-U, Higgins S, et al. (2016) Calibration and validation of APSIM-wheat and CERES-wheat for spring wheat under rainfed conditions: models evaluation and application. Computers and Electronics in Agriculture 123, 384-401.
| Crossref | Google Scholar |

Archontoulis SV, Miguez FE, Moore KJ (2014a) Evaluating APSIM maize, soil water, soil nitrogen, manure, and soil temperature modules in the Midwestern United States. Agronomy Journal 106, 1025-1040.
| Crossref | Google Scholar |

Archontoulis SV, Miguez FE, Moore KJ (2014b) A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: application to soybean. Environmental Modelling & Software 62, 465-477.
| Crossref | Google Scholar |

Asseng S, Keating BA, Fillery IRP, Gregory PJ, Bowden JW, Turner NC, et al. (1998) Performance of the APSIM-wheat model in Western Australia. Field Crops Research 57, 163-179.
| Crossref | Google Scholar |

Asseng S, van Keulen H, Stol W (2000) Performance and application of the APSIM Nwheat model in the Netherlands. European Journal of Agronomy 12, 37-54.
| Crossref | Google Scholar |

Asseng S, Turner NC, Keating BA (2001) Analysis of water-and nitrogen-use efficiency of wheat in a Mediterranean climate. Plant and Soil 233, 127-143.
| Crossref | Google Scholar |

Balboa GR, Archontoulis SV, Salvagiotti F, Garcia FO, Stewart WM, Francisco E, et al. (2019) A systems-level yield gap assessment of maize-soybean rotation under high-and low-management inputs in the Western US Corn Belt using APSIM. Agricultural Systems 174, 145-154.
| Crossref | Google Scholar |

Barrett-Lennard EG, D’Antuono M, George N, Holmes KW, Ward PR (2024) Rainfall, reference evapotranspiration and fertiliser application are the main variables affecting crop yield in National Variety Trials under rainfed conditions across southern Australia. Crop & Pasture Science In press.
| Google Scholar |

Blunden J, Boyer T (2021) A look at 2020: takeaway points from the state of the climate. Bulletin of the American Meteorological Society 102, 756-768.
| Crossref | Google Scholar |

Brown H, Huth N, Holzworth D (2018) Crop model improvement in APSIM: using wheat as a case study. European Journal of Agronomy 100, 141-150.
| Crossref | Google Scholar |

California Soil Resources Lab (2020) Streaming soil survey data. California Soil Resources Laboratory, University of California, Davis. Available at http://casoilresource.lawr.ucdavis.edu/drupal/node/538

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.
| Crossref | Google Scholar | PubMed |

CDFA (2022) California agricultural statistics review, 2021–2022. California Department of Food and Agriculture. Sacramento, CA.

Ceglar A, Črepinšek Z, Kajfež-Bogataj L, Pogačar T (2011) The simulation of phenological development in dynamic crop model: the Bayesian comparison of different methods. Agricultural and Forest Meteorology 151, 101-115.
| Crossref | Google Scholar |

Chenu K, Porter JR, Martre P, Basso B, Chapman SC, Ewert F, et al. (2017) Contribution of crop models to adaptation in wheat. Trends in Plant Science 22, 472-490.
| Crossref | Google Scholar | PubMed |

Chenu K, Van Oosterom EJ, McLean G, Deifel KS, Fletcher A, Geetika G, et al. (2018) Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals. Journal of Experimental Botany 69, 3181-3194.
| Crossref | Google Scholar | PubMed |

CIMIS (2020) California irrigation management information system. California Department of Water Resources Office of Water Use Efficiency, Sacramento, CA. Available at http://wwwcimis.water.ca.gov/cimis/data.jsp

Dubcovsky J (2020) Distinguished Professor, Department of Plant Sciences, the University of California, Davis. (Ed. NA George). Available at https://dubcovskylab.ucdavis.edu/home

Dubcovsky J, Loukoianov A, Fu D, Valarik M, Sanchez A, Yan L (2006) Effect of photoperiod on the regulation of wheat vernalization genes VRN1 and VRN2. Plant Molecular Biology 60, 469-480.
| Crossref | Google Scholar | PubMed |

Farré I, Robertson MJ, Walton GH, Asseng S (2002) Simulating phenology and yield response of canola to sowing date in Western Australia using the APSIM model. Australian Journal of Agricultural Research 53, 1155-1164.
| Crossref | Google Scholar |

George N, Kaffka S (2017) Canola as a new crop for California: a simulation study. Agronomy Journal 109, 496-509.
| Crossref | Google Scholar |

George N, Lundy M (2019) Quantifying genotype × environment effects in long-term common wheat yield trials from an agroecologically diverse production region. Crop Science 59, 1960-1972.
| Crossref | Google Scholar |

George N, Rodriguez M, Nelsen T, Lundy M, Mathesius K, Leinfelder-Miles M, et al. (2017) 2017 regional common wheat & triticale, durum wheat, and barley performance tests in California. University of California, Agriculture and Natural Resources. Davis, CA.

George N, Thompson SE, Hollingsworth J, Orloff S, Kaffka S (2018) Measurement and simulation of water-use by canola and camelina under cool-season conditions in California. Agricultural Water Management 196, 15-23.
| Crossref | Google Scholar |

Hammer GL, McLean G, van Oosterom E, Chapman S, Zheng B, Wu A, et al. (2020) Designing crops for adaptation to the drought and high-temperature risks anticipated in future climates. Crop Science 60, 605-621.
| Crossref | Google Scholar |

Harris F, Martin P, Eagles H (2016) Understanding wheat phenology: flowering response to sowing time. GRDC Grains Research Update, Wagga, NSW. Available at https://grdc.com.au/Research-and-Development/GRDC-Update-Papers/2016/02/Understanding-wheat-phenologyflowering-response-to-sowing-time

Harter T (2020) California’s 2014 sustainable groundwater management act–from the back seat to the driver seat in the (inter) national groundwater sustainability movement. In ‘Sustainable groundwater management’. (Eds S Barnett, C Holley, M Montginoul, J-D Rinaudo) pp. 511–536. (Springer)

He L, Zhao G, Jin N, Zhuang W, Yu Q (2015) Global sensitivity analysis of APSIM-Wheat parameters in different climate zones and yield levels. Transactions of the Chinese Society of Agricultural Engineering 31, 148-157.
| Crossref | Google Scholar |

Hebbali A (2020) olsrr: tools for building OLS regression models. R package version 0.5.3. Available at https://CRAN.R-project.org/package=olsrr

Hochman Z, van Rees H, Carberry PS, Hunt JR, McCown RL, Gartmann A, et al. (2009a) Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate. Crop & Pasture Science 60, 1057-1070.
| Crossref | Google Scholar |

Hochman Z, Holzworth D, Hunt JR (2009b) Potential to improve on-farm wheat yield and WUE in Australia. Crop & Pasture Science 60, 708-716.
| Crossref | Google Scholar |

Holzworth DP, Huth NI, deVoil PG, Zurcher EJ, Herrmann NI, McLean G, et al. (2014) APSIM – evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327-350.
| Crossref | Google Scholar |

Holzworth D, Huth NI, Fainges J, Brown H, Zurcher E, Cichota R, et al. (2018) APSIM next generation: overcoming challenges in modernising a farming systems model. Environmental Modelling & Software 103, 43-51.
| Crossref | Google Scholar |

Hussain J, Khaliq T, Ahmad A, Akhtar J (2018) Performance of four crop model for simulations of wheat phenology, leaf growth, biomass and yield across planting dates. PLoS ONE 13, e0197546.
| Crossref | Google Scholar | PubMed |

Jackson L, Fernandez B, Meister H, Spiller M (2006) Small grain production manual. p. 110. University of California, Division of Agriculture and Natural Resources, Oakland, CA, USA.

Keating BA (2020) Crop, soil and farm systems models – science, engineering or snake oil revisited. Agricultural Systems 184, 102903.
| Crossref | Google Scholar |

Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, et al. (2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267-288.
| Crossref | Google Scholar |

Kotir J, Bell L, Kirkegaard J, Whish J, Atkins KA (2020) Farm resource use as influenced by diverse cropping systems in the Australian Northern cropping zone. In ‘The 64th Annual Conference of the Australasian Agricultural and Resource Economics Society (AARES)’. Perth, Western Australia, 12–14 February 2020. p. 5. (Australian Agricultural and Resource Economics Society)

Kouadio L, Newlands N, Potgieter A, McLean G, Hill H (2015) Exploring the potential impacts of climate variability on spring wheat yield with the APSIM decision support tool. Agricultural Sciences 6, 686-698.
| Crossref | Google Scholar |

Lawes RA, Oliver YM, Huth NI (2019) Optimal nitrogen rate can be predicted using average yield and estimates of soil water and leaf nitrogen with infield experimentation. Agronomy Journal 111, 1155-1164.
| Crossref | Google Scholar |

Meier E, Lilley J, Kirkegaard J, Whish J, McBeath T (2020) Management practices that maximise gross margins in Australian canola (Brassica napus L.). Field Crops Research 252, 107803.
| Crossref | Google Scholar |

Muller B, Martre P (2019) Plant and crop simulation models: powerful tools to link physiology, genetics, and phenomics. Journal of Experimental Botany 70(9), 2339-2344.
| Crossref | Google Scholar |

Nelsen T, Rodriguez M, George N, Lundy M, Mathesius K, Leinfelder-Miles M, et al. (2018) 2017-18 regional common wheat & triticale, durum wheat, and barley performance tests in California. University of California, Agriculture and Natural Resources, Davis, CA.

Nelsen T, McCullough E, Lundy M, Mathesius K, Leinfelder-Miles M, Light S, et al. (2019) 2018-19 regional common wheat & triticale, durum wheat, and barley performance tests in California. University of California, Agriculture and Natural Resources.

Ottman MJ, Anthony Hunt L, White JW (2013) Photoperiod and vernalization effect on anthesis date in winter-sown spring wheat regions. Agronomy Journal 105, 1017-1025.
| Crossref | Google Scholar |

Pasquel D, Roux S, Richetti J, Cammarano D, Tisseyre B, Taylor JA (2022) A review of methods to evaluate crop model performance at multiple and changing spatial scales. Precision Agriculture 23, 1489-1513.
| Crossref | Google Scholar |

Pathak TB, Maskey ML, Dahlberg JA, Kearns F, Bali KM, Zaccaria D (2018) Climate change trends and impacts on California agriculture: a detailed review. Agronomy 8, 25.
| Crossref | Google Scholar |

Peake AS, Huth NI, Carberry PS, Raine SR, Smith RJ (2014) Quantifying potential yield and lodging-related yield gaps for irrigated spring wheat in sub-tropical Australia. Field Crops Research 158, 1-14.
| Crossref | Google Scholar |

Peel MC, Finlayson BL, Mcmahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences Discussions 4, 439-473.
| Crossref | Google Scholar |

R Core Team (2020) ‘R: a language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna, Austria) Available at https://www.R-project.org/

Ramirez-Villegas J, Molero Milan A, Alexandrov N, Asseng S, Challinor AJ, Crossa J, et al. (2020) CGIAR modeling approaches for resource-constrained scenarios: I. Accelerating crop breeding for a changing climate. Crop Science 60, 547-567.
| Crossref | Google Scholar |

Reardon-Smith K, Mushtaq S, Farley HS, Cliffe N, Stone RC, Ostini J, et al. (2015) Virtual discussions to support climate risk decision making on farms. Journal of Economic and Social Policy 17, 7-1-7-12.
| Google Scholar |

Robertson MJ, Rebetzke GJ, Norton RM (2015) Assessing the place and role of crop simulation modelling in Australia. Crop & Pasture Science 66, 877-893.
| Crossref | Google Scholar |

Sadras VO (2020) On water-use efficiency, boundary functions, and yield gaps: French and Schultz insight and legacy. Crop Science 60, 2187-2191.
| Crossref | Google Scholar |

Therneau T, Atkinson B (2019) rpart: recursive partitioning and regression trees. R package version 4.1-15. Available at https://CRAN.R-project.org/package=rpart

USDA NASS (2020) United States Department of Agriculture National Agricultural Statistics Service. Washington, DC. Available at http://www.nass.usda.gov/

Wallach D, Makowski D, Jones JW, Brun F (2019) ‘Working with dynamic crop models: methods, tools and examples for agriculture and environment.’ 3rd edn. (Elsevier)

Williams AP, Cook BI, Smerdon JE (2022) Rapid intensification of the emerging southwestern North American megadrought in 2020–2021. Nature Climate Change 12, 232-234.
| Crossref | Google Scholar |

Yan W (2014) ‘Crop variety trials: data management and analysis.’ 1st edn. (John Wiley & Sons)

Ye Z, Qiu X, Chen J, Cammarano D, Ge Z, Ruane AC, et al. (2020) Impacts of 1.5° C and 2.0° C global warming above pre-industrial on potential winter wheat production of China. European Journal of Agronomy 120, 126149.
| Crossref | Google Scholar |

Yunusa IAM, Bellotti WD, Moore AD, Probert ME, Baldock JA, Miyan SM (2004) An exploratory evaluation of APSIM to simulate growth and yield processes for winter cereals in rotation systems in South Australia. Australian Journal of Experimental Agriculture 44, 787-800.
| Crossref | Google Scholar |

Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Research 14(6), 415-421.
| Crossref | Google Scholar |

Zambrano-Bigiarini M (2020) hydroGOF: goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.4-0. Available at https://github.com/hzambran/hydroGOF

Zhao G, Bryan BA, Song X (2014) Sensitivity and uncertainty analysis of the APSIM-wheat model: interactions between cultivar, environmental, and management parameters. Ecological Modelling 279, 1-11.
| Crossref | Google Scholar |

Zhao C, Liu B, Xiao L, Hoogenboom G, Boote KJ, Kassie BT, et al. (2019) A SIMPLE crop model. European Journal of Agronomy 104, 97-106.
| Crossref | Google Scholar |

Zhao P, Zhou Y, Li F, Ling X, Deng N, Peng S, et al. (2020) The adaptability of APSIM-wheat model in the middle and lower reaches of the Yangtze River Plain of China: a case study of winter wheat in Hubei Province. Agronomy 10, 981.
| Crossref | Google Scholar |

Zheng B, Chenu K, Fernanda Dreccer M, Chapman SC (2012) Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties? Global Change Biology 18, 2899-2914.
| Crossref | Google Scholar | PubMed |

Zheng B, Chenu K, Doherty A, Chapman S (2015) The APSIM-wheat module (7.5R3008). Available at https://www.apsim.info/wp-content/uploads/2019/09/WheatDocumentation.pdf