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ORIGINAL RESEARCH article

Front. Sustain. Food Syst.
Sec. Climate-Smart Food Systems
Volume 8 - 2024 | doi: 10.3389/fsufs.2024.1423702
This article is part of the Research Topic Modelling Approaches for Climate Variability and Change Mitigation and Adaptation in Resource Constrained Farming Systems View all 11 articles

Maximizing Farm Resilience: The Effect of Climate Smart Agricultural Adoption Practices on Food Performance Amid Adverse Weather Events

Provisionally accepted
Raza A. Tunio Raza A. Tunio *Dongmei Li Dongmei Li *Nawab Khan Nawab Khan
  • College of Management, Sichuan Agricultural University, Chengdu, China

The final, formatted version of the article will be published soon.

    Introduction: Global climate change (CC) significantly impacts sustainable food systems and the agricultural sector, primarily through increasing adverse weather events. This study aims to explore the adaptation strategies farmers use to address these challenges and evaluate the effectiveness of climate-smart agricultural (CSA) practices on food performance.We collected data from 720 crop farmers located in three provinces of Pakistan using a random sampling method. To address potential biases, this study employed the endogenous switching regression (ESR) model. This model effectively addresses endogeneity and selection bias by considering both observable and unobservable characteristics.Study findings indicate that CSA practices substantially enhance net farm returns, reduce volatility, and mitigate downside risks. The analysis also highlights key features affecting the acceptance of CSA practices, including higher education, age, climate information, and availability of agricultural extension services.Discussion: These insights are essential for policymakers, offering a framework for informed decision-making to tackle CC's effects on food production, improve living standards, and enhance global food security.

    Keywords: Adoption, Food security, Climate Change, crop challenges, Endogenous switching regression

    Received: 26 Apr 2024; Accepted: 08 Aug 2024.

    Copyright: © 2024 Tunio, Li and Khan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Raza A. Tunio, College of Management, Sichuan Agricultural University, Chengdu, China
    Dongmei Li, College of Management, Sichuan Agricultural University, Chengdu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.