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Assessing drought and its impacts on wheat yield using remotely sensed observations in rainfed Potohar region of Pakistan

Author

Listed:
  • Muhammad Ijaz

    (Global Change Impact Studies Centre (GCISC), Ministry of Climate Change (MoCC))

  • Qudsia Zafar

    (Global Change Impact Studies Centre (GCISC), Ministry of Climate Change (MoCC))

  • Aftab Ahmad Khan

    (Global Change Impact Studies Centre (GCISC), Ministry of Climate Change (MoCC))

  • Sher Shah Hassan

    (Global Change Impact Studies Centre (GCISC), Ministry of Climate Change (MoCC))

Abstract

Drought is a serious threat to agriculture particularly of rainfed regions like Potohar region of Pakistan. Wheat, staple food crop of this region, is mostly affected by drought, thereby impacting food security of the region. Effective drought monitoring and its impacts on wheat production have therefore been the key concerns of the farmers and policy makers in order to plan for any upcoming food crises in the region. A proactive drought management approach is needed in this regard. This study aims to assist the decision-making process for drought monitoring and yield predictions, as it informs drought assessment and its impacts on crop yield using drought and vegetation indices along with climate and crop yield data. This research quantifies recurrent drought events for Rubi (wheat crop) season (November–April) from 2000 to 2018 in the Potohar region using indices such as standardized precipitation index (SPI), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI) along with climatic parameters, i.e., mean temperature, rainfall and soil moisture. Results show a strong positive correlation of wheat yield with rainfall (r = 0.97) and soil moisture (r = 0.88), while a strong negative correlation with temperature (r = − 0.98). Three moderate (2000–01, 2001–02, 2009–10) and two weak (2011–12, 2017–18) drought events are identified using SPI, whereas two more drought events (2007–08 and 2016–17) are noticed when vegetation indices are used. Looking at Oceanic Niño Index (ONI), no definite pattern relating to ongoing La-Nina or El-Nino conditions during the specific drought years is observed for the Potohar region. Artificial neural network (ANN), a multilayer perception (MLP) model, is applied afterward to see the individual impact of each study parameter on wheat yield. Soil moisture is found to impact yield by 100%, temperature as 74%, rainfall as 61% and then rest of the indices. The findings of this study not only provide a scientific base for future studies on drought indicators, but assert that a cumulative approach is needed for effective drought management at national level.

Suggested Citation

  • Muhammad Ijaz & Qudsia Zafar & Aftab Ahmad Khan & Sher Shah Hassan, 2023. "Assessing drought and its impacts on wheat yield using remotely sensed observations in rainfed Potohar region of Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3699-3721, April.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:4:d:10.1007_s10668-022-02200-1
    DOI: 10.1007/s10668-022-02200-1
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    References listed on IDEAS

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    1. Ahmad, Munir & Siftain, Hassan & Iqbal, Muhammad, 2014. "Impact of Climate Change on Wheat Productivity in Pakistan: A District Level Analysis," MPRA Paper 72859, University Library of Munich, Germany.
    2. Ahmad, Munir & Nawaz, Muhammad & Iqbal, Muhammad & Javed, Sajid, 2014. "Analysing the Impact of Climate Change on Rice Productivity in Pakistan," MPRA Paper 72861, University Library of Munich, Germany.
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