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Laser-land leveling adoption and its impact on water use, crop yields and household income: Empirical evidence from the rice-wheat system of Pakistan Punjab

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  • Ali, Akhter
  • Hussain, Imtiaz
  • Rahut, Dil Bahadur
  • Erenstein, Olaf

Abstract

Using a primary dataset from 350 farmers from the rice-wheat area of Pakistan Punjab, we estimate the impact of laser-land leveling on water saving, crop yields and household income. The analysis employs propensity score matching (PSM) to correct for the potential sample selection bias that may arise due to systematic differences between adopting and non-adopting farmers. In the study, 57% of farmers had access to laser land leveling, with an important role for service providers. Adoption of laser-land leveling has a positive impact on irrigation water savings, wheat and rice yields and household income. The study suggests policy implications for making laser land leveling access and performance more socially inclusive through enhanced awareness, institutional support to service providers and public-private partnerships.

Suggested Citation

  • Ali, Akhter & Hussain, Imtiaz & Rahut, Dil Bahadur & Erenstein, Olaf, 2018. "Laser-land leveling adoption and its impact on water use, crop yields and household income: Empirical evidence from the rice-wheat system of Pakistan Punjab," Food Policy, Elsevier, vol. 77(C), pages 19-32.
    • Handle: RePEc:eee:jfpoli:v:77:y:2018:i:c:p:19-32
    DOI: 10.1016/j.foodpol.2018.03.018
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    Cited by:

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    4. Devkota, Krishna Prasad & Yadav, Sudhir & Humphreys, E. & Kumar, Akhilesh & Kumar, Pankaj & Kumar, Virender & Malik, R.K. & Srivastava, Amit K., 2021. "Land gradient and configuration effects on yield, irrigation amount and irrigation water productivity in rice-wheat and maize-wheat cropping systems in Eastern India," Agricultural Water Management, Elsevier, vol. 255(C).
    5. Ruzzante, Sacha & Labarta, Ricardo & Bilton, Amy, 2021. "Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature," World Development, Elsevier, vol. 146(C).
    6. Naeem Hayat & Abdullah Al Mamun & Noorul Azwin Md Nasir & Ganeshsree Selvachandran & Noorshella Binti Che Nawi & Quek Shio Gai, 2020. "Predicting Sustainable Farm Performance—Using Hybrid Structural Equation Modelling with an Artificial Neural Network Approach," Land, MDPI, vol. 9(9), pages 1-37, August.
    7. Jeetendra Prakash Aryal & Dil Bahadur Rahut & Tek B. Sapkota & Ritika Khurana & Arun Khatri-Chhetri, 2020. "Climate change mitigation options among farmers in South Asia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 3267-3289, April.

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