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Modeling Potential production and yield gap of potato using modelling and GIS approaches

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  • Dadrasi, Amir
  • Torabi, Benjamin
  • Rahimi, Asghar
  • Soltani, Afshin
  • Zeinali, Ebrahim

Abstract

Understanding yield potential (Yp) and yield gap (Yg) in current intensive potato (solanum tuberosum L.) production is essential to meet future food demand with the limited resources. Evaluating yield gap is a strong approach to estimate maximum production potential when all factors are in the best condition. A complete estimation of yield gap and potential yield across all major potato producing regions in Iran is lacking. The global yield gap atlas (GYGA) protocol was used to estimate potential yield of potato in Iran. This protocol is based on the climatic zones (CZs) and the reference weather stations (RWS) buffer zones, soil types in each buffer zone. Thirty-five RWS buffer zones in potato producing regions were selected, and total potato area in the RWS buffer zones covered 83% of the whole potato harvest area. According to the results, the average Yp was 67.3 t ha–1 and actual yield (Ya) was 30 t ha–1. Therefore, the average tuber yield gap was 37.3 t ha–1. These results indicate that the Potato producers achieved 45% of the potential yield in Iran. Iranian farmers produced 5 million tons of potato from about 164,000 ha. If they can obtain only 80% of Yp (53.8 t ha–1), amount of potato production will be 8.8 million tones. As result, they can produce 5.2 million tons tuber yield of potato in 97,000 ha cultivation area. Thus, with closing yield gap and increasing potato production, it is possible to decrease potato lands.

Suggested Citation

  • Dadrasi, Amir & Torabi, Benjamin & Rahimi, Asghar & Soltani, Afshin & Zeinali, Ebrahim, 2022. "Modeling Potential production and yield gap of potato using modelling and GIS approaches," Ecological Modelling, Elsevier, vol. 471(C).
    • Handle: RePEc:eee:ecomod:v:471:y:2022:i:c:s0304380022001600
    DOI: 10.1016/j.ecolmodel.2022.110050
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    References listed on IDEAS

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    1. You, Liangzhi & Wood, Stanley & Wood-Sichra, Ulrike & Wu, Wenbin, 2014. "Generating global crop distribution maps: From census to grid," Agricultural Systems, Elsevier, vol. 127(C), pages 53-60.
    2. Razzaghi, Fatemeh & Zhou, Zhenjiang & Andersen, Mathias N. & Plauborg, Finn, 2017. "Simulation of potato yield in temperate condition by the AquaCrop model," Agricultural Water Management, Elsevier, vol. 191(C), pages 113-123.
    3. Soltani, A. & Alimagham, S.M. & Nehbandani, A. & Torabi, B. & Zeinali, E. & Dadrasi, A. & Zand, E. & Ghassemi, S. & Pourshirazi, S. & Alasti, O. & Hosseini, R.S. & Zahed, M. & Arabameri, R. & Mohammad, 2020. "SSM-iCrop2: A simple model for diverse crop species over large areas," Agricultural Systems, Elsevier, vol. 182(C).
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    Cited by:

    1. Rui-Feng Wang & Wen-Hao Su, 2024. "The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review," Agriculture, MDPI, vol. 14(8), pages 1-30, July.
    2. Diaz-Gonzalez, Freddy A. & Vuelvas, Jose. & Vallejo, Victoria E. & Patino, D., 2023. "Fertilization rate optimization model for potato crops to maximize yield while reducing polluting nitrogen emissions," Ecological Modelling, Elsevier, vol. 485(C).

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