IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i7p1372-d1189757.html
   My bibliography  Save this article

Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece

Author

Listed:
  • Melpomeni Nikou

    (Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
    Soil and Water Resources Institute, Hellenic Agricultural Organization (H.A.O.)—“DEMETER”, 570 01 Thessaloniki, Greece)

  • Theodoros Mavromatis

    (Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

Abstract

Yield estimations at global or regional spatial scales have been compromised due to poor crop model calibration. A methodology for estimating the genetic parameters related to grain growth and yield for the CERES-Wheat crop model is proposed based on yield gap concept, the GLUE coefficient estimator, and the global yield gap atlas (GYGA). Yield trials with three durum wheat cultivars in an experimental farm in northern Greece from 2004 to 2010 were used. The calibration strategy conducted with CERES-Wheat (embedded in DSSAT v.4.7.5) on potential mode taking into account the year-to-year variability of relative yield gap Yrg (YgC_adj) was: (i) more effective than using the average site value of Yrg (YgC_unadj) only (the relative RMSE ranged from 10 to 13% for the YgC_adj vs. 48 to 57% for YgC_unadj) and (ii) superior (slightly inferior) to the strategy conducted with DSSAT v.4.7.5 (DSSAT v.3.5—relative RMSE of 5 to 8% were found) on rainfed mode. Earlier anthesis, maturity, and decreased potential yield (from 2.2 to 3.9% for 2021–2050, and from 5.0 to 7.1% for 2071–2100), due to increased temperature and solar radiation, were found using an ensemble of 11 EURO-CORDEX regional climate model simulations. In conclusion, the proposed strategy provides a scientifically robust guideline for crop model calibration that minimizes input requirements due to operating the crop model on potential mode. Further testing of this methodology is required with different plants, crop models, and environments.

Suggested Citation

  • Melpomeni Nikou & Theodoros Mavromatis, 2023. "Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece," Land, MDPI, vol. 12(7), pages 1-19, July.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1372-:d:1189757
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/7/1372/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/7/1372/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Romanowicz, Renata J. & Beven, Keith J., 2006. "Comments on generalised likelihood uncertainty estimation," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1315-1321.
    2. Timsina, J. & Humphreys, E., 2006. "Performance of CERES-Rice and CERES-Wheat models in rice-wheat systems: A review," Agricultural Systems, Elsevier, vol. 90(1-3), pages 5-31, October.
    3. 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.
    4. Mary Ollenburger & Page Kyle & Xin Zhang, 2022. "Uncertainties in estimating global potential yields and their impacts for long-term modeling," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(5), pages 1177-1190, October.
    5. Bao, Yawen & Hoogenboom, Gerrit & McClendon, Ron & Vellidis, George, 2017. "A comparison of the performance of the CSM-CERES-Maize and EPIC models using maize variety trial data," Agricultural Systems, Elsevier, vol. 150(C), pages 109-119.
    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. Mahboobe Ghobadi & Mahdi Gheysari & Mohammad Shayannejad & Hamze Dokoohaki, 2023. "Analyzing the Effects of Planting Date on the Uncertainty of CERES-Maize and Its Potential to Reduce Yield Gap in Arid and Mediterranean Climates," Agriculture, MDPI, vol. 13(8), pages 1-17, July.
    2. Gupta, Rishabh & Mishra, Ashok, 2019. "Climate change induced impact and uncertainty of rice yield of agro-ecological zones of India," Agricultural Systems, Elsevier, vol. 173(C), pages 1-11.
    3. Bohan, David & Schmucki, Reto & Abay, Abrha & Termansen, Mette & Bane, Miranda & Charalabiis, Alice & Cong, Rong-Gang & Derocles, Stephane & Dorner, Zita & Forster, Matthieu & Gibert, Caroline & Harro, 2020. "Designing farmer-acceptable rotations that assure ecosystem service provision inthe face of climate change," MPRA Paper 112313, University Library of Munich, Germany.
    4. Anshuman Gunawat & Devesh Sharma & Aditya Sharma & Swatantra Kumar Dubey, 2022. "Assessment of climate change impact and potential adaptation measures on wheat yield using the DSSAT model in the semi-arid environment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 2077-2096, March.
    5. Adam, M. & Wery, J. & Leffelaar, P.A. & Ewert, F. & Corbeels, M. & Van Keulen, H., 2013. "A systematic approach for re-assembly of crop models: An example to simulate pea growth from wheat growth," Ecological Modelling, Elsevier, vol. 250(C), pages 258-268.
    6. Dennis Junior Choruma & Frank Chukwuzuoke Akamagwuna & Nelson Oghenekaro Odume, 2022. "Simulating the Impacts of Climate Change on Maize Yields Using EPIC: A Case Study in the Eastern Cape Province of South Africa," Agriculture, MDPI, vol. 12(6), pages 1-24, May.
    7. Utset, Angel & Velicia, Herminio & del Rio, Blanca & Morillo, Rodrigo & Centeno, Jose Antonio & Martinez, Juan Carlos, 2007. "Calibrating and validating an agrohydrological model to simulate sugarbeet water use under mediterranean conditions," Agricultural Water Management, Elsevier, vol. 94(1-3), pages 11-21, December.
    8. Attia, Ahmed & El-Hendawy, Salah & Al-Suhaibani, Nasser & Alotaibi, Majed & Tahir, Muhammad Usman & Kamal, Khaled Y., 2021. "Evaluating deficit irrigation scheduling strategies to improve yield and water productivity of maize in arid environment using simulation," Agricultural Water Management, Elsevier, vol. 249(C).
    9. Timsina, J. & Wolf, J. & Guilpart, N. & van Bussel, L.G.J. & Grassini, P. & van Wart, J. & Hossain, A. & Rashid, H. & Islam, S. & van Ittersum, M.K., 2018. "Can Bangladesh produce enough cereals to meet future demand?," Agricultural Systems, Elsevier, vol. 163(C), pages 36-44.
    10. Paleari, Livia & Movedi, Ermes & Zoli, Michele & Burato, Andrea & Cecconi, Irene & Errahouly, Jabir & Pecollo, Eleonora & Sorvillo, Carla & Confalonieri, Roberto, 2021. "Sensitivity analysis using Morris: Just screening or an effective ranking method?," Ecological Modelling, Elsevier, vol. 455(C).
    11. Wang, Zhiqiang & Ye, Li & Jiang, Jingyi & Fan, Yida & Zhang, Xiaoran, 2022. "Review of application of EPIC crop growth model," Ecological Modelling, Elsevier, vol. 467(C).
    12. Kadiyala, M.D.M. & Jones, J.W. & Mylavarapu, R.S. & Li, Y.C. & Reddy, M.D., 2015. "Identifying irrigation and nitrogen best management practices for aerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models," Agricultural Water Management, Elsevier, vol. 149(C), pages 23-32.
    13. Cai, Liping & Wang, Hui & Liu, Yanxu & Fan, Donglin & Li, Xiaoxiao, 2022. "Is potential cultivated land expanding or shrinking in the dryland of China? Spatiotemporal evaluation based on remote sensing and SVM," Land Use Policy, Elsevier, vol. 112(C).
    14. Serra, J. & Paredes, P. & Cordovil, CMdS & Cruz, S. & Hutchings, NJ & Cameira, MR, 2023. "Is irrigation water an overlooked source of nitrogen in agriculture?," Agricultural Water Management, Elsevier, vol. 278(C).
    15. Timsina, J. & Buresh, R.J. & Dobermann, A. & Dixon, J. (ed.), 2011. "Rice-maize systems in Asia: current situation and potential," IRRI Books, International Rice Research Institute (IRRI), number 164490.
    16. Malik, Wafa & Dechmi, Farida, 2019. "DSSAT modelling for best irrigation management practices assessment under Mediterranean conditions," Agricultural Water Management, Elsevier, vol. 216(C), pages 27-43.
    17. Kothari, Kritika & Ale, Srinivasulu & Bordovsky, James P. & Thorp, Kelly R. & Porter, Dana O. & Munster, Clyde L., 2019. "Simulation of efficient irrigation management strategies for grain sorghum production over different climate variability classes," Agricultural Systems, Elsevier, vol. 170(C), pages 49-62.
    18. Woli, Prem & Hoogenboom, Gerrit & Alva, Ashok, 2016. "Simulation of potato yield, nitrate leaching, and profit margins as influenced by irrigation and nitrogen management in different soils and production regions," Agricultural Water Management, Elsevier, vol. 171(C), pages 120-130.
    19. 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).
    20. Shi, Yinfang & Wang, Zhaoyang & Hou, Cheng & Zhang, Puhan, 2022. "Yield estimation of Lycium barbarum L. based on the WOFOST model," Ecological Modelling, Elsevier, vol. 473(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:gam:jlands:v:12:y:2023:i:7:p:1372-:d:1189757. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.