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.

讨论了影响采用现代水稻品种并影响印度奥里萨邦贫困的因素。通过多阶段采样技术，来自奥里萨邦Cuttack和Sambalpur地区的363户家庭参加了调查。使用克雷格（Cragg）的双关卡模型对稻米的MV的采用和采用强度的决定因素进行建模，倾向得分匹配用于分析采用对贫困的影响。结果表明，年龄，教育程度，规避风险，土地面积，单产，对MV的高产感知，对疾病的抵抗力和MV的可获得性对采用该决定产生积极影响。但是，这些变量包括家庭人数，农民的经验，非农工作参与，获得的信贷额，种子成本，杀虫剂和肥料对MV的采用产生负面影响。MV的采用强度受到农民经验，肥料成本和MV适销性的不利影响，并受到家庭规模，风险规避，土地面积，杀虫剂成本，对MV的高产量和MV种子的可利用性的正面影响。 。非采纳者对MV的采用者的贫困发生率，差距和严重性很高。在使用近邻匹配，分层匹配，半径匹配和核匹配四种不同算法对MV组的采用者和非采用者进行匹配之后，MV采用的影响导致人均每月家庭支出增加了约52.82美元至63.17美元。 。化肥的成本和MV的适销性，并受到家庭人数，风险规避，土地面积，杀虫剂成本，对MV的感知以及高产量和MV种子的获取等方面的积极影响。非采用者对MV的采用者的贫困发生率，差距和严重性很高。在使用近邻匹配，分层匹配，半径匹配和核匹配四种不同算法对MV组的采用者和非采用者进行匹配之后，MV采用的影响导致人均每月家庭支出增加了约52.82美元至63.17美元。 。化肥成本和MV的适销性，并受到家庭人数，风险规避，土地面积，杀虫剂成本，对MV的高产和MV种子可用性的积极影响。非采用者对MV的采用者的贫困发生率，差距和严重性很高。在使用近邻匹配，分层匹配，半径匹配和核匹配四种不同算法对MV组的采用者和非采用者进行匹配之后，MV采用的影响导致人均每月家庭支出增加了约52.82美元至63.17美元。 。