IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v476y2023ics0304380022003313.html
   My bibliography  Save this article

Effect of variation in the observations on the prediction uncertainty in crop model simulation: Use ORYZA (v3) as a case study

Author

Listed:
  • Ling, Xiaoxia
  • Deng, Nanyan
  • Xiong, Dongliang
  • Yuan, Shen
  • Peng, Shaobing
  • Li, Tao
  • Huang, Jianliang

Abstract

The reliability of crop models for predicting ecophysiological processes determines applicability of models in modern agricultural research and production. However, many uncertainties in the modeling processes are strangling the extension of model application. Model parameterization is one of the major sources of prediction uncertainties, which depends on the Observations used for model Calibration (OC). In this study, we conducted a two-year field experiment to evaluate the effect of variation in OC on model prediction error and uncertainty. Model performance was analyzed after the calibration of each dataset. We found that model bias was the dominant factor for the overall prediction error with the low variation of OC. Model uncertainty would contribute more to overall prediction error when OC increased to a threshold of circa 14.5%. The low variation of OC resulted in a lower variation of prediction error (0.04%–26.8%) than that of the high variation of OC (0.4%–71.8%). The variation of prediction error was different across environments that model simulated. Besides, the prediction error and uncertainty of in-season variables, such as biomass and leaf area index, etc., were also more significant than end-season variables, such as final grain yield. Our findings highlighted that the variability in observations was a major contributor to model prediction uncertainty during the calibration process. We recommend that efforts in uncertainty reduction should include quantitatively assessing in the suitability of datasets for the purpose of calibration, together with making a specific calibration strategy based on the simulation targets.

Suggested Citation

  • Ling, Xiaoxia & Deng, Nanyan & Xiong, Dongliang & Yuan, Shen & Peng, Shaobing & Li, Tao & Huang, Jianliang, 2023. "Effect of variation in the observations on the prediction uncertainty in crop model simulation: Use ORYZA (v3) as a case study," Ecological Modelling, Elsevier, vol. 476(C).
  • Handle: RePEc:eee:ecomod:v:476:y:2023:i:c:s0304380022003313
    DOI: 10.1016/j.ecolmodel.2022.110233
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022003313
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.110233?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. Confalonieri, Roberto & Bregaglio, Simone & Acutis, Marco, 2016. "Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration," Ecological Modelling, Elsevier, vol. 328(C), pages 72-77.
    2. Chen, Ning & Li, Xianyue & Shi, Haibin & Hu, Qi & Zhang, Yuehong & Hou, Chenli & Liu, Yahui, 2022. "Modeling evapotranspiration and evaporation in corn/tomato intercropping ecosystem using a modified ERIN model considering plastic film mulching," Agricultural Water Management, Elsevier, vol. 260(C).
    3. Nanyan Deng & Patricio Grassini & Haishun Yang & Jianliang Huang & Kenneth G. Cassman & Shaobing Peng, 2019. "Closing yield gaps for rice self-sufficiency in China," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    4. Bouman, B.A.M. & van Laar, H.H., 2006. "Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions," Agricultural Systems, Elsevier, vol. 87(3), pages 249-273, March.
    5. Feng, Liping & Bouman, B. A.M. & Tuong, T.P. & Cabangon, R.J. & Li, Yalong & Lu, Guoan & Feng, Yuehua, 2007. "Exploring options to grow rice using less water in northern China using a modelling approach: I. Field experiments and model evaluation," Agricultural Water Management, Elsevier, vol. 88(1-3), pages 1-13, March.
    6. Guo, Shibo & Zhang, Zhentao & Guo, Erjing & Fu, Zhenzhen & Gong, Jingjin & Yang, Xiaoguang, 2022. "Historical and projected impacts of climate change and technology on soybean yield in China," Agricultural Systems, Elsevier, vol. 203(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. Amarasingha, R.P.R.K. & Suriyagoda, L.D.B. & Marambe, B. & Gaydon, D.S. & Galagedara, L.W. & Punyawardena, R. & Silva, G.L.L.P. & Nidumolu, U. & Howden, M., 2015. "Simulation of crop and water productivity for rice (Oryza sativa L.) using APSIM under diverse agro-climatic conditions and water management techniques in Sri Lanka," Agricultural Water Management, Elsevier, vol. 160(C), pages 132-143.
    2. Grotelüschen, Kristina & Gaydon, Donald S. & Langensiepen, Matthias & Ziegler, Susanne & Kwesiga, Julius & Senthilkumar, Kalimuthu & Whitbread, Anthony M. & Becker, Mathias, 2021. "Assessing the effects of management and hydro-edaphic conditions on rice in contrasting East African wetlands using experimental and modelling approaches," Agricultural Water Management, Elsevier, vol. 258(C).
    3. Yu, Qianan & Cui, Yuanlai, 2022. "Improvement and testing of ORYZA model water balance modules for alternate wetting and drying irrigation," Agricultural Water Management, Elsevier, vol. 271(C).
    4. Antonopoulos, Vassilis Z., 2010. "Modelling of water and nitrogen balances in the ponded water and soil profile of rice fields in Northern Greece," Agricultural Water Management, Elsevier, vol. 98(2), pages 321-330, December.
    5. Wang, Weiguang & Yu, Zhongbo & Zhang, Wei & Shao, Quanxi & Zhang, Yiwei & Luo, Yufeng & Jiao, Xiyun & Xu, Junzeng, 2014. "Responses of rice yield, irrigation water requirement and water use efficiency to climate change in China: Historical simulation and future projections," Agricultural Water Management, Elsevier, vol. 146(C), pages 249-261.
    6. Tian, Zhan & Zhong, Honglin & Sun, Laixiang & Fischer, Günther & van Velthuizen, Harrij & Liang, Zhuoran, 2014. "Improving performance of Agro-Ecological Zone (AEZ) modeling by cross-scale model coupling: An application to japonica rice production in Northeast China," Ecological Modelling, Elsevier, vol. 290(C), pages 155-164.
    7. Jing, Qi & Keulen, Herman van & Hengsdijk, Huib, 2010. "Modeling biomass, nitrogen and water dynamics in rice-wheat rotations," Agricultural Systems, Elsevier, vol. 103(7), pages 433-443, September.
    8. Jing, Qi & Bouman, Bas & van Keulen, Herman & Hengsdijk, Huib & Cao, Weixing & Dai, Tingbo, 2008. "Disentangling the effect of environmental factors on yield and nitrogen uptake of irrigated rice in Asia," Agricultural Systems, Elsevier, vol. 98(3), pages 177-188, October.
    9. Dutta, S. K & Laing, Alison M. & Kumar, S. & Gathala, Mahesh K. & Singh, Ajoy K. & Gaydon, D.S. & Poulton, P., 2020. "Improved water management practices improve cropping system profitability and smallholder farmers’ incomes," Agricultural Water Management, Elsevier, vol. 242(C).
    10. Movedi, Ermes & Valiante, Daniele & Colosio, Alessandro & Corengia, Luca & Cossa, Stefano & Confalonieri, Roberto, 2022. "A new approach for modeling crop-weed interaction targeting management support in operational contexts: A case study on the rice weeds barnyardgrass and red rice," Ecological Modelling, Elsevier, vol. 463(C).
    11. Zhongen Niu & Huimin Yan & Fang Liu, 2020. "Decreasing Cropping Intensity Dominated the Negative Trend of Cropland Productivity in Southern China in 2000–2015," Sustainability, MDPI, vol. 12(23), pages 1-14, December.
    12. Senthilkumar, K. & Bindraban, P.S. & Thiyagarajan, T.M. & de Ridder, N. & Giller, K.E., 2008. "Modified rice cultivation in Tamil Nadu, India: Yield gains and farmers' (lack of) acceptance," Agricultural Systems, Elsevier, vol. 98(2), pages 82-94, September.
    13. Yuqun Dong & Yaming Zhuang, 2024. "How does single- or double-cropped rice policy influence spatially irrigated land value in China?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 70(6), pages 279-290.
    14. Luoman Pu & Junnan Jiang & Menglu Ma & Duan Huang, 2024. "Gaps between Rice Actual and Potential Yields Based on the VPM and GAEZ Models in Heilongjiang Province, China," Agriculture, MDPI, vol. 14(2), pages 1-22, February.
    15. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    16. Jing, Qi & Bélanger, Gilles & Baron, Vern & Bonesmo, Helge & Virkajärvi, Perttu & Young, David, 2012. "Regrowth simulation of the perennial grass timothy," Ecological Modelling, Elsevier, vol. 232(C), pages 64-77.
    17. Cai, Ximing & Yang, Yi-Chen E. & Ringler, Claudia & Zhao, Jianshi & You, Liangzhi, 2011. "Agricultural water productivity assessment for the Yellow River Basin," Agricultural Water Management, Elsevier, vol. 98(8), pages 1297-1306, May.
    18. Ahmad Numery Ashfaqul Haque & Md. Kamal Uddin & Muhammad Firdaus Sulaiman & Adibah Mohd Amin & Mahmud Hossain & Zakaria M. Solaiman & Azharuddin Abd Aziz & Mehnaz Mosharrof, 2022. "Combined Use of Biochar with 15 Nitrogen Labelled Urea Increases Rice Yield, N Use Efficiency and Fertilizer N Recovery under Water-Saving Irrigation," Sustainability, MDPI, vol. 14(13), pages 1-21, June.
    19. Edgar Vladimir Gutiérrez Castorena & Gustavo Andrés Ramírez Gómez & Carlos Alberto Ortíz Solorio, 2023. "The Agricultural Potential of a Region with Semi-Dry, Warm and Temperate Subhumid Climate Diversity through Agroecological Zoning," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    20. Chen, Ning & Li, Xianyue & Shi, Haibin & Yan, Jianwen & Zhang, Yuehong & Hu, Qi, 2023. "Evaluating the effects of plastic film mulching duration on soil nitrogen dynamic and comprehensive benefit for corn (Zea mays L.) field," Agricultural Water Management, Elsevier, vol. 286(C).

    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:ecomod:v:476:y:2023:i:c:s0304380022003313. 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.journals.elsevier.com/ecological-modelling .

    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.