Adoption and Impact of Modern Rice Varieties on Poverty in Eastern India

作     者：Richard Kwasi BANNOR Gupta Amarnath Krishna KUMAR Helena OPPONG-KYEREMEH Camillus Abawiera WONGNAA Richard Kwasi BANNOR;Gupta Amarnath Krishna KUMAR;Helena OPPONG-KYEREMEH;Camillus Abawiera WONGNAA

作者机构：Department of Agricultural EconomicsAgribusiness and ExtensionUniversity of Energy and Natural ResourcesSunyaniGhana Indian Council of Agricultural Research-National Rice Research InstituteCuttack 753006OrissaIndia Department of Agricultural EconomicsAgribusiness and ExtensionKwame Nkrumah University of Science and TechnologyKumasiGhana 

出 版 物：《Rice science》 (水稻科学（英文版）)

年 卷 期：2020年第27卷第1期

页      面：56-66页

核心收录：

学科分类：0710[理学-生物学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 09[农学] 0901[农学-作物学] 0836[工学-生物工程] 0902[农学-园艺学] 

基　　金：supported by Federation of Indian Chambers of Commerce & Industry (2017–2018) through the Government of India under CV Raman Post-Doctoral Fellowship for African Researchers 

主　　题：rice adoption poverty Cragg's Double hurdle model modern variety 

摘      要：The factors affecting the adoption of modern varieties(MVs) of rice and impact on poverty in Odisha, India were discussed. A total of 363 households from Cuttack and Sambalpur districts of Odisha via multistage sampling technique participated in the survey. The Cragg s Double hurdle model was used to model the determinants of adoption and intensity of adoption of MVs of rice, and the propensity score matching was used to analyze the impact of adoption on poverty. The results showed that age, education, risk aversion, land size, yield, perception of MVs as high yielding, resistant to diseases and availability of MVs positively influenced the decision to adopt. However, variables such as household size, experience of a farmer, off-farm job participation, amount of credit received, cost of seeds, insecticides and fertilizers negatively influenced the adoption of MVs. Intensity of adoption of MVs was negatively influenced by experience of a farmer, cost of fertilizer and marketability of MVs, and positively affected by household size, risk aversion, land size, cost of insecticides, perception of MVs as high yielding and availability of MV seeds. Poverty incidence, gap and severity were high among non-adopters to adopters of MVs. After matching adopters and non-adopters of MV groups using four different algorithms of nearest neighbour matching, stratification matching, radius matching and kernel matching, the impact of MV adoption resulted in higher per capita monthly household expenditure by about US$ 52.82 to US$ 63.17.