Determinants of Rice Production, Its Economic Contribution to Household Income and Poverty Alleviation in Guinea: Case Study Prefecture of Faranah

Siba Kolin Koivogui, Yonggong Liu, John Chrisostom Pesha, Feng Ba, Yang Gao


In Guinea, agricultural potential is enormous; the land potential is estimated at 6.2 million hectares of which 1.6 million hectares are cultivated only. Rice is amongst the most important cash crops in the agricultural sector and contributes to 77% of cereal production in the country. The study aims at examining rice production in order to assess its contribution on farmers’ income in Faranah Prefecture, Guinea. The study involved 270 respondents from eight rural communes plus Faranah center (Bagna, Beindou, Heremakono, Nialia, Passayah, Sandenia, Songoyah, Tiro and Faranah center). Data were collected taking into account the status of producers in each area: small (0-2.5 ha), medium (2.6 -5.5 ha) and large producers (> 5.5 ha).The regression model was used for 11 variables (gender, age, education, farm experience, household size, farm size, labor cost, fertilizer, capital inputs, household income and training where F values of all producer categories were F = 48.831 ***, F = 4.52 * and F = 2.39 * and the corresponding values indicated R2=0.807; R2= 0.417 and R2=0.317respectively. The gross margin analysis shows that incomes of each producer category were between (4639 - 10553 US$/ha), (2781-14866 US$) and 8903.77- 1557 US$/ha) respectively. The benefit cost ratio (BCR) of each producer categories were at (1.8–2. 07), (4 - 1.8) and (1.2 - 1.7) respectively. In sum, it would be concluded that the level of productivity remains lower compared to the size of land being accessed by the rice farmers. Therefore, the study recommends that the Government of Guinea should strengthen its institutions in order to improve the quality of agricultural land, stabilize the rice market and allocate more resources for improving rice farmer’s asset.

Keywords: Rice producer, profitability, Faranah prefecture, linear regression model, poverty alleviation

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