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Usage of SUBSTOR model in potato yield prediction

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  • Stastná, M.
  • Toman, F.
  • Dufková, J.

Abstract

The study focused on evaluation of SUBSTOR-Potato model and its utilization in potato grows management. The experimental field used for the model evaluation was located in Zabcice--South Moravian region with altitudes of 179m above the sea level. Tuber yield served as reference for the model evaluation. Nine years experimental data set (1994-2002) was used for the model evaluation. Rosara cultivar represented very early growing potato (Solanum tuberosum L.) in the experiment. Comparison between observed and simulated tuber yields presented the evaluation process of SUBSTOR-Potato model. Tuber yields simulated by the model showed excellent accuracy (R2=0.97), but only for four of nine tested years (1997, 1998, 1999 and 2002). The model tended to underestimate the tuber yield for unsuitable conditions (i.e. dry years--low amount of precipitation and its disordered distribution during the growing season or higher mean air temperature). Study proved SUBSTOR-Potato model as suitable for utilization in potato management; however, potential differences might be expected while using the model under extreme weather conditions.

Suggested Citation

  • Stastná, M. & Toman, F. & Dufková, J., 2010. "Usage of SUBSTOR model in potato yield prediction," Agricultural Water Management, Elsevier, vol. 97(2), pages 286-290, February.
    • Handle: RePEc:eee:agiwat:v:97:y:2010:i:2:p:286-290
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    1. 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.
    2. Liao, Xiaolin & Su, Zhihua & Liu, Guodong & Zotarelli, Lincoln & Cui, Yuqi & Snodgrass, Crystal, 2016. "Impact of soil moisture and temperature on potato production using seepage and center pivot irrigation," Agricultural Water Management, Elsevier, vol. 165(C), pages 230-236.
    3. Jarosław Kurek & Gniewko Niedbała & Tomasz Wojciechowski & Bartosz Świderski & Izabella Antoniuk & Magdalena Piekutowska & Michał Kruk & Krzysztof Bobran, 2023. "Prediction of Potato ( Solanum tuberosum L.) Yield Based on Machine Learning Methods," Agriculture, MDPI, vol. 13(12), pages 1-25, December.
    4. Popova, Zornitsa & Pereira, Luis S., 2011. "Modelling for maize irrigation scheduling using long term experimental data from Plovdiv region, Bulgaria," Agricultural Water Management, Elsevier, vol. 98(4), pages 675-683, February.
    5. Vashisht, B.B. & Nigon, T. & Mulla, D.J. & Rosen, C. & Xu, H. & Twine, T. & Jalota, S.K., 2015. "Adaptation of water and nitrogen management to future climates for sustaining potato yield in Minnesota: Field and simulation study," Agricultural Water Management, Elsevier, vol. 152(C), pages 198-206.
    6. Grados, D. & García, S. & Schrevens, E., 2020. "Assessing the potato yield gap in the Peruvian Central Andes," Agricultural Systems, Elsevier, vol. 181(C).
    7. Woli, Prem & Hoogenboom, Gerrit, 2018. "Simulating weather effects on potato yield, nitrate leaching, and profit margin in the US Pacific Northwest," Agricultural Water Management, Elsevier, vol. 201(C), pages 177-187.

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