Volume 12, Issue 25, January - June 2026

Identifying Determinants of Adopting Solar Pump Irrigation in Rice Cultivation: A Climate Smart Adaptation Strategy

Masuda Islam Limu¹, Muhammad Humayun Kabir^2♦, Md. Abul Bashar²

¹MS student, Department of agricultural extension and information system, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh
²Professor, Department of agricultural extension and information system, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh

^♦Corresponding author
Muhammad Humayun Kabir, Professor, Department of agricultural extension and information system, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh

ABSTRACT

Solar pump irrigation (SPI) can be a game-changing phenomenon in the Bangladesh irrigation sector through its wider adoption among the farmers. The objectives of this study were to assess the rate of SPI adoption and the contributing factors of adopting solar pump irrigation in rice cultivation. The study was conducted with 108 randomly selected farmers in Maheshpur upazila under Jhenaidah district, Bangladesh. A pre-tested interview schedule was developed to collect data from the selected respondents. Data were analyzed through mean, percentage, frequency, standard deviation, and multinomial logistic regression. The interview survey revealed that the highest proportion (72.2%) of respondents did not adopt SPI, while 11.1% and 16.7% adopted it fully and partially, respectively. The inferential analysis revealed that family labor availability, land ownership status, on-farm income, and attitude towards SPI significantly influence farmers’ non-adoption of SPI. Again, level of education, family labor availability, on-farm income, and off-farm income significantly influenced farmers’ partial adoption of SPI. Initiatives to improve farmers’ knowledge and foster a favorable attitude towards SPI might help promote the widespread adoption of this climate-smart strategy throughout the country.

Keywords: Rice cultivation, solar pump irrigation, adoption, climate smart practice, multinomial logistic regression

Discovery Agriculture, 2026, 12, e16da3216

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Published: 06 June 2026