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<p>South Punjab, Pakistan, faces severe climate change impacts. This study examined agroforestry (AF) adoption (AFA) through a structured survey of 240 (n = 3 × 2 ×4 × 10 = 240, 3 districts × 2 tehsils × 4 villages × 10 respondents) AF farmers across Bahawalpur, Lodhran and Muzaffargarh. Results revealed 100% of farmers perceived climate change as a threat, with the highest AFA among 41–60 age-group (33.75–37.5%, <i>p</i> &lt; 0.05) and higher among formally educated farmers (15–28.75%). Higher total-annual-family-income (TAFI) groups demonstrated greater AFA, with the middle-income group (150,000–300,000 PKR) exhibiting the highest adoption rates (40–45%), while access to bank credit significantly enhanced AFA (18.75–30%). Most respondents were owner-cultivators (65–80%) and nearly two-third possessed landholdings between 5–15 acres. Simple linear regression revealed strong positive relationship between AFA and traditional education (R<sup>2</sup> = 0.80, 0.90 and 0.11), TAFI (R<sup>2</sup> = 0.97, 0.90 and 0.96) and landholdings (R<sup>2</sup> = 0.68, 0.57 and 0.72), moderate correlation for household status (R<sup>2 =</sup> 0.52, 0.54, and 0.62) but negative relationship for tenancy status (R<sup>2</sup> = 0.37, 0.75 and 0.50) across Bahawalpur, Lodhran and Muzaffargarh, respectively. Subsequently, multiple linear regression (F(6, 233) = 100.1, <i>p</i> &lt; 0.001) explained 72.1% of AFA variance (Adjusted R<sup>2</sup> = 0.713), identifying household structure, TAFI, and landholding size as key predictors, while tenancy status showed a strong negative association. Logistic regression model, with 94.2% classification accuracy and (McFadden R<sup>2</sup> = 0.73, and Hosmer–Lemeshow <i>p</i> = 0.452, validated these results, demonstrating the 36.97-fold increase in AFA with larger landholdings, whereas tenancy status reduced odds by 90% (OR = 0.108). Finally, SWOT analysis highlighted potential of AF in Southern Punjab and necessitated policy, technology and capacity-building to address systemic challenges and enhance resilience. Hence, AFA presented a viable strategy for climate change mitigation in South Punjab.</p>

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Socioeconomic drivers and farmers’ perception of climate change impact on agroforestry adoption: a regression-based study in South Punjab, Pakistan

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Muhammad Safeer,
  • Muhammad Zubair,
  • Tanveer Hussain,
  • Muhammad Ammar Khan,
  • Muhammad Madnee,
  • Turkan Hasanova Allahverdi

摘要

South Punjab, Pakistan, faces severe climate change impacts. This study examined agroforestry (AF) adoption (AFA) through a structured survey of 240 (n = 3 × 2 ×4 × 10 = 240, 3 districts × 2 tehsils × 4 villages × 10 respondents) AF farmers across Bahawalpur, Lodhran and Muzaffargarh. Results revealed 100% of farmers perceived climate change as a threat, with the highest AFA among 41–60 age-group (33.75–37.5%, p < 0.05) and higher among formally educated farmers (15–28.75%). Higher total-annual-family-income (TAFI) groups demonstrated greater AFA, with the middle-income group (150,000–300,000 PKR) exhibiting the highest adoption rates (40–45%), while access to bank credit significantly enhanced AFA (18.75–30%). Most respondents were owner-cultivators (65–80%) and nearly two-third possessed landholdings between 5–15 acres. Simple linear regression revealed strong positive relationship between AFA and traditional education (R2 = 0.80, 0.90 and 0.11), TAFI (R2 = 0.97, 0.90 and 0.96) and landholdings (R2 = 0.68, 0.57 and 0.72), moderate correlation for household status (R2 = 0.52, 0.54, and 0.62) but negative relationship for tenancy status (R2 = 0.37, 0.75 and 0.50) across Bahawalpur, Lodhran and Muzaffargarh, respectively. Subsequently, multiple linear regression (F(6, 233) = 100.1, p < 0.001) explained 72.1% of AFA variance (Adjusted R2 = 0.713), identifying household structure, TAFI, and landholding size as key predictors, while tenancy status showed a strong negative association. Logistic regression model, with 94.2% classification accuracy and (McFadden R2 = 0.73, and Hosmer–Lemeshow p = 0.452, validated these results, demonstrating the 36.97-fold increase in AFA with larger landholdings, whereas tenancy status reduced odds by 90% (OR = 0.108). Finally, SWOT analysis highlighted potential of AF in Southern Punjab and necessitated policy, technology and capacity-building to address systemic challenges and enhance resilience. Hence, AFA presented a viable strategy for climate change mitigation in South Punjab.