IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v301y2024ics0378377424002737.html
   My bibliography  Save this article

The importance of model structure and soil data detail on the simulations of crop growth and water use: A case study for sugarcane

Author

Listed:
  • dos Santos Vianna, Murilo
  • Metselaar, Klaas
  • de Jong van Lier, Quirijn
  • Gaiser, Thomas
  • Marin, Fábio Ricardo

Abstract

Process-based crop models have faced rapid development over the last years, and many modelling platforms are now available and can be used in a wide range of conditions. Whilst the selection of a model should be suited to the purpose of its application, very few studies focused on the impact of choosing different model structures and data details on the simulation outputs. One important aspect is the soil water dynamics, which can be simulated at different levels of details in terms of data and approaches. In this study, we investigated the impact of model structure and data detail on simulations of sugarcane growth and irrigation scheduling. Three different soil water routines (Standalone, Tipping-Bucket, SWAP) were coupled with the SAMUCA model and calibrated with a comprehensive field experiment dataset. We also tested the influence of using simplified homogeneous (SL) and detailed (DL) soil profile information in model performance. The model framework was evaluated against independent field experiments across Brazil and used to simulate long-term sugarcane growth and irrigation scheduling. After calibration, the SWAP-DL showed the highest accuracy in soil moisture predictions, with a 6 % error (RRMSE), but the difference from TippingBucket-DL was small (8 %). While the performance of stalk dry mass, LAI and water-use efficiency simulations were within the range found in literature, comprehensive field experiments showing significant impacts of drought on sugarcane growth are still lacking for a more rigorous evaluation. Both SWAP and tipping-bucket approaches showed higher robustness to soil data detail as compared to the Standalone method, which should be avoided when soil water is critical for sugarcane growth. The use of tipping-bucket method may still be preferred when the research goal is focused on crop growth and soil parameters are limited. SWAP-SAMUCA may provide an extended ability to represent agrohydrological processes in sugarcane plantations and process understanding.

Suggested Citation

  • dos Santos Vianna, Murilo & Metselaar, Klaas & de Jong van Lier, Quirijn & Gaiser, Thomas & Marin, Fábio Ricardo, 2024. "The importance of model structure and soil data detail on the simulations of crop growth and water use: A case study for sugarcane," Agricultural Water Management, Elsevier, vol. 301(C).
  • Handle: RePEc:eee:agiwat:v:301:y:2024:i:c:s0378377424002737
    DOI: 10.1016/j.agwat.2024.108938
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377424002737
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2024.108938?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Keating, Brian A., 2020. "Crop, soil and farm systems models – science, engineering or snake oil revisited," Agricultural Systems, Elsevier, vol. 184(C).
    2. Nóia Júnior, Rogério de S. & Asseng, Senthold & García-Vila, Margarita & Liu, Ke & Stocca, Valentina & dos Santos Vianna, Murilo & Weber, Tobias K.D. & Zhao, Jin & Palosuo, Taru & Harrison, Matthew To, 2023. "A call to action for global research on the implications of waterlogging for wheat growth and yield," Agricultural Water Management, Elsevier, vol. 284(C).
    3. Goldemberg, José & Mello, Francisco F.C. & Cerri, Carlos E.P. & Davies, Christian A. & Cerri, Carlos C., 2014. "Meeting the global demand for biofuels in 2021 through sustainable land use change policy," Energy Policy, Elsevier, vol. 69(C), pages 14-18.
    4. Victor Meriguetti Pinto & Jos C. van Dam & Quirijn de Jong van Lier & Klaus Reichardt, 2019. "Intercropping Simulation Using the SWAP Model: Development of a 2×1D Algorithm," Agriculture, MDPI, vol. 9(6), pages 1-19, June.
    5. P. Kaelo & M. M. Ali, 2006. "Some Variants of the Controlled Random Search Algorithm for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 130(2), pages 253-264, August.
    6. de Wit, Allard & Boogaard, Hendrik & Fumagalli, Davide & Janssen, Sander & Knapen, Rob & van Kraalingen, Daniel & Supit, Iwan & van der Wijngaart, Raymond & van Diepen, Kees, 2019. "25 years of the WOFOST cropping systems model," Agricultural Systems, Elsevier, vol. 168(C), pages 154-167.
    7. Jarvis, Nicholas & Larsbo, Mats & Lewan, Elisabet & Garré, Sarah, 2022. "Improved descriptions of soil hydrology in crop models: The elephant in the room?," Agricultural Systems, Elsevier, vol. 202(C).
    8. Fabian Stenzel & Peter Greve & Wolfgang Lucht & Sylvia Tramberend & Yoshihide Wada & Dieter Gerten, 2021. "Irrigation of biomass plantations may globally increase water stress more than climate change," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    9. de Jong van Lier, Quirijn & Wendroth, Ole & van Dam, Jos C., 2015. "Prediction of winter wheat yield with the SWAP model using pedotransfer functions: An evaluation of sensitivity, parameterization and prediction accuracy," Agricultural Water Management, Elsevier, vol. 154(C), pages 29-42.
    10. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
    11. Singels, A. & Paraskevopoulos, A.L. & Mashabela, M.L., 2019. "Farm level decision support for sugarcane irrigation management during drought," Agricultural Water Management, Elsevier, vol. 222(C), pages 274-285.
    12. Heinen, Marius & Mulder, Martin & van Dam, Jos & Bartholomeus, Ruud & de Jong van Lier, Quirijn & de Wit, Janine & de Wit, Allard & Hack - ten Broeke, Mirjam, 2024. "SWAP 50 years: Advances in modelling soil-water-atmosphere-plant interactions," Agricultural Water Management, Elsevier, vol. 298(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heinen, Marius & Mulder, Martin & van Dam, Jos & Bartholomeus, Ruud & de Jong van Lier, Quirijn & de Wit, Janine & de Wit, Allard & Hack - ten Broeke, Mirjam, 2024. "SWAP 50 years: Advances in modelling soil-water-atmosphere-plant interactions," Agricultural Water Management, Elsevier, vol. 298(C).
    2. Brombacher, Joost & Silva, Isadora Rezende de Oliveira & Degen, Jelle & Pelgrum, Henk, 2022. "A novel evapotranspiration based irrigation quantification method using the hydrological similar pixels algorithm," Agricultural Water Management, Elsevier, vol. 267(C).
    3. Bailly, Hugo & Mortier, Frédéric & Giraud, Gaël, 2024. "Empirical analysis of a debt-augmented Goodwin model for the United States," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 619-633.
    4. Linghua Qiu & Junhao He & Chao Yue & Philippe Ciais & Chunmiao Zheng, 2024. "Substantial terrestrial carbon emissions from global expansion of impervious surface area," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    5. Telmo José Mendes & Diego Silva Siqueira & Eduardo Barretto Figueiredo & Ricardo de Oliveira Bordonal & Mara Regina Moitinho & José Marques Júnior & Newton La Scala Jr., 2021. "Soil carbon stock estimations: methods and a case study of the Maranhão State, Brazil," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16410-16427, November.
    6. Joachim Eisenberg & Fabrice A. Muvundja, 2020. "Quantification of Erosion in Selected Catchment Areas of the Ruzizi River (DRC) Using the (R)USLE Model," Land, MDPI, vol. 9(4), pages 1-18, April.
    7. Sarah R. Weiskopf & Forest Isbell & Maria Isabel Arce-Plata & Moreno Di Marco & Mike Harfoot & Justin Johnson & Susannah B. Lerman & Brian W. Miller & Toni Lyn Morelli & Akira S. Mori & Ensheng Weng &, 2024. "Biodiversity loss reduces global terrestrial carbon storage," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Peter Bossew & Giorgia Cinelli & Giancarlo Ciotoli & Quentin G. Crowley & Marc De Cort & Javier Elío Medina & Valeria Gruber & Eric Petermann & Tore Tollefsen, 2020. "Development of a Geogenic Radon Hazard Index—Concept, History, Experiences," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
    9. Ravic Nijbroek & Kristin Piikki & Mats Söderström & Bas Kempen & Katrine G. Turner & Simeon Hengari & John Mutua, 2018. "Soil Organic Carbon Baselines for Land Degradation Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity in Otjozondjupa, Namibia," Sustainability, MDPI, vol. 10(5), pages 1-20, May.
    10. Fritz, Steffen & See, Linda & Bayas, Juan Carlos Laso & Waldner, François & Jacques, Damien & Becker-Reshef, Inbal & Whitcraft, Alyssa & Baruth, Bettina & Bonifacio, Rogerio & Crutchfield, Jim & Rembo, 2019. "A comparison of global agricultural monitoring systems and current gaps," Agricultural Systems, Elsevier, vol. 168(C), pages 258-272.
    11. Amirhossein Hassani & Adisa Azapagic & Nima Shokri, 2021. "Global predictions of primary soil salinization under changing climate in the 21st century," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    12. Yu Feng & Zhenzhong Zeng & Timothy D. Searchinger & Alan D. Ziegler & Jie Wu & Dashan Wang & Xinyue He & Paul R. Elsen & Philippe Ciais & Rongrong Xu & Zhilin Guo & Liqing Peng & Yiheng Tao & Dominick, 2022. "Doubling of annual forest carbon loss over the tropics during the early twenty-first century," Nature Sustainability, Nature, vol. 5(5), pages 444-451, May.
    13. Amintas Brandão Jr. & Lisa Rausch & América Paz Durán & Ciniro Costa Jr. & Seth A. Spawn & Holly K. Gibbs, 2020. "Estimating the Potential for Conservation and Farming in the Amazon and Cerrado under Four Policy Scenarios," Sustainability, MDPI, vol. 12(3), pages 1-22, February.
    14. Bughici, Theodor & Skaggs, Todd H. & Corwin, Dennis L. & Scudiero, Elia, 2022. "Ensemble HYDRUS-2D modeling to improve apparent electrical conductivity sensing of soil salinity under drip irrigation," Agricultural Water Management, Elsevier, vol. 272(C).
    15. Antoine Arnoud & Fatih Guvenen & Tatjana Kleineberg, 2023. "Benchmarking Global Optimizers," Working Papers 801, Federal Reserve Bank of Minneapolis.
    16. Katzin, David & van Henten, Eldert J. & van Mourik, Simon, 2022. "Process-based greenhouse climate models: Genealogy, current status, and future directions," Agricultural Systems, Elsevier, vol. 198(C).
    17. Fang, Yan Ru & Hossain, MD Shouquat & Peng, Shuan & Han, Ling & Yang, Pingjian, 2024. "Sustainable energy development of crop straw in five southern provinces of China: Bioenergy production, land, and water saving potential," Renewable Energy, Elsevier, vol. 224(C).
    18. Wähling, Lara-Sophie & Fridahl, Mathias & Heimann, Tobias & Merk, Christine, 2023. "The sequence matters: Expert opinions on policy mechanisms for bioenergy with carbon capture and storage," Open Access Publications from Kiel Institute for the World Economy 275739, Kiel Institute for the World Economy (IfW Kiel).
    19. Shuai Ren & Tao Wang & Bertrand Guenet & Dan Liu & Yingfang Cao & Jinzhi Ding & Pete Smith & Shilong Piao, 2024. "Projected soil carbon loss with warming in constrained Earth system models," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    20. Shen Tan & Shengchao Qiao & Han Wang & Sheng Chang, 2024. "Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles," Agriculture, MDPI, vol. 14(11), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:301:y:2024:i:c:s0378377424002737. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.