Cultivation of Local Rice Varieties in Bangladesh: Assessing the Farm Level Determinants - Md Saiful Islam; Mohammad Chhiddikur Rahman; Md Enamul Haque; Md Shajedur Rahaman; Md Imran Omar; Md A R Sarkar; Mohammad A Islam (Journal of Bangladesh Agricultural University, 2023)
One of the main objectives of the green revolution is to replace the local (traditional) cultivars with high-yielding varieties (HYVs). Replacing HYVs in the local cultivars-intensive areas would increase rice production and strengthen national food security. To do this, it is necessary to identify the determinants of cultivating local rice varieties by farmers. Therefore, this study used fractional logistic regression and a cognitive approach in the popular Aman and Boro season local rice variety-intensive areas to find out the causes of cultivating local rice varieties. The findings reveal that the main cause is the adverse ecology, where HYVs are not suitable, but local cultivars have physiological attributes to be well suited. The local cultivars are growing mainly in the Haor and flood-prone ecosystems, where modern varieties can not be sustained. The profitability of local rice cultivars is better than the HYVs as well. Moreover, farmers' socioeconomic conditions such as age, education, occupation, income, and farm size influence the cultivation of local rice varieties. The yield, land topography, and market price also have an impact on the adoption intensity of local cultivars. The farmers also opined that the special grain quality of the local rice cultivars attracts the farmers, consumers, and market price that lubricate the intensity of local rice varieties adoption in the specific areas of Bangladesh. The findings of this study would be helpful for the rice breeders, scientists, and policymakers to develop suitable modern rice cultivars and management packages for boosting rice production in the targeted local rice cultivars intensive areas of Bangladesh.
Keywords: 630; Traditional rice cultivars; Adoption intensity; Profitability; Fractional logistic regression