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Rural Farmers’ Cognition and Climate Change Adaptation Impact on Cash Crop Productivity: Evidence from a Recent Study

Author

Listed:
  • Nawab Khan

    (College of Management, Sichuan Agricultural University, Chengdu 611130, China
    These authors contributed equally to this work.)

  • Jiliang Ma

    (Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    These authors contributed equally to this work.)

  • Hazem S. Kassem

    (Department of Agricultural Extension and Rural Society, King Saud University, Riyadh 11451, Saudi Arabia)

  • Rizwan Kazim

    (College of Management, Sichuan Agricultural University, Chengdu 611130, China)

  • Ram L. Ray

    (College of Agriculture and Human Sciences, Prairie View A&M University, Prairie View, TX 77446, USA)

  • Muhammad Ihtisham

    (School of Agriculture, Forestry and Food Engineering, Yibin University, Yibin 644000, China
    College of Horticulture and Forestry, Huazhong Agricultural University, Wuhan 430070, China)

  • Shemei Zhang

    (College of Management, Sichuan Agricultural University, Chengdu 611130, China)

Abstract

The world faces a once-in-a-century transformation due to the COVID-19 pandemic, adversely affecting farmers’ employment, production practices, and livelihood resilience. Meanwhile, climate change (CC) is a crucial issue limiting agricultural production worldwide. Farmers’ lives, severely affected by extreme weather conditions, are resulting in the reduced production of major economic crops. The CC has drastically influenced the major agricultural sectors of Pakistan, leading to a significant decline in farmers’ living standards and the overall economy. Climate-smart and eco-friendly agricultural practices can mitigate greenhouse gas emissions and ameliorate agricultural productivity under extreme environmental conditions. This paper highlights farmers’ autonomous CC adaptation strategies and their influence on cash crop (maize for this study) yield under prevailing circumstances. The current study used a simultaneous equation model to examine the different adaptation impacts on adapters and non-adapters. The survey results of 498 maize farmers in rural Pakistan revealed that growers were aware of the recent CC and had taken adequate adaptive measures to acclimatize to CC. Farmers’ arable land area, awareness level, and information accessibility to CC are the most crucial factors that impart a significant role in their adaptation judgments. However, most growers have inadequate adaptation strategies, including improved irrigation and the utilization of extensive fertilizers and pesticides. Using a simultaneous equation model of endogenous switching regression, the study found that farmers not adapted to CC were negatively affecting maize productivity. Therefore, this study suggests that policymakers pay attention to the countermeasures farmers have not taken to mitigate the impact of CC. In addition, policymakers should deliver appropriate adaptation strategies to assist growers in coping with climate-related natural hazards and ensure farmers’ livelihood security, rural revitalization, and sustainable agricultural development.

Suggested Citation

  • Nawab Khan & Jiliang Ma & Hazem S. Kassem & Rizwan Kazim & Ram L. Ray & Muhammad Ihtisham & Shemei Zhang, 2022. "Rural Farmers’ Cognition and Climate Change Adaptation Impact on Cash Crop Productivity: Evidence from a Recent Study," IJERPH, MDPI, vol. 19(19), pages 1-16, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12556-:d:931361
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    References listed on IDEAS

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    2. Shipeng Yang & Wanxiang Xu & Yuxuan Xie & Muhammad Tayyab Sohail & Yefang Gong, 2023. "Impact of Natural Hazards on Agricultural Production Decision Making of Peasant Households: On the Basis of the Micro Survey Data of Hunan Province," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
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    4. Jiaxu Ling & Yongji Xue & Chenyujing Yang & Yuanyuan Zhang, 2022. "Effect of Farmers’ Awareness of Climate Change on Their Willingness to Adopt Low-Carbon Production: Based on the TAM-SOR Model," IJERPH, MDPI, vol. 20(1), pages 1-17, December.

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