| 秦巴山特困区农户生计资本及生计策略研究——以商洛市为例 |
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| 引用本文： | 刘倩,张戬,何艳冰,杨新军. 秦巴山特困区农户生计资本及生计策略研究——以商洛市为例[J]. 干旱区地理, 2020, 43(1): 237-247. DOI: 10.12118/j.issn.1000-6060.2020.01.27 |
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| 作者姓名： | 刘倩 张戬 何艳冰 杨新军 |
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| 作者单位： | 1 重庆师范大学地理与旅游学院，重庆401331；2 西北大学城市与环境学院，陕西西安710127； 3 河南理工大学建筑与艺术设计学院，河南焦作454000 |
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| 基金项目： | 国家自然科学基金项目（41771574）；重庆师范大学基金项目(19XLB008) |
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| 摘 要： | 基于秦巴山商洛地区农户问卷调查数据,在可持续生计框架下,聚焦不同群体之间生计资本状况,并探讨其农户生计资本对生计策略选择的影响以及生计资本的耦合性。结果表明:(1)山区农户生计策略出现明显分化,依据非农收入比重分为纯务工型、务工主导型、兼业型和纯农型4种类型。(2)调研样本中农户生计资本有限和不均衡,呈现金融资本和社会资本相对较高,自然资本、人力资本偏低的特征。非贫困户中兼业型生计资本总值最高,务工主导型、纯务工型次之,纯农型最低;贫困户中务工主导型生计资本总值最高,纯务工型、兼业型次之,纯农型最低。(3)非贫困户中人均耕地面积、人均林地面积、耕地质量、职业技能水平、政治资源、就业网络对纯务工型农户向务工主导型、兼业型转变有着积极影响,家庭人均收入、男性劳动力比例则具有负向影响;家庭人均收入和职业技能水平对于纯务工型向纯农型转变有负向影响。贫困户中人均耕地面积、人均林地面积、政治资源对纯务工型农户向务工主导型、兼业型和纯农型转变具有正向影响,家庭人均收入、劳动力教育水平、职业技能水平、联系成本则具有负向影响。(4)非贫困农户生计资本耦合度依次为兼业型>务工主导型>纯务工型&...
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| 关 键 词： | 农户 生计资本 生计策略 秦巴山区 |
| 收稿时间： | 2019-07-24 |
Livelihood capital and livelihood strategies of the farmer household in the exceptional poverty regions of Qinling-Daba mountainous area:A case of Shangluo City |
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| LIU Qian,ZHANG Jian,HE Yan-bing,YANG Xin-jun. Livelihood capital and livelihood strategies of the farmer household in the exceptional poverty regions of Qinling-Daba mountainous area:A case of Shangluo City[J]. Arid Land Geography, 2020, 43(1): 237-247. DOI: 10.12118/j.issn.1000-6060.2020.01.27 |
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| Authors: | LIU Qian ZHANG Jian HE Yan-bing YANG Xin-jun |
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| Affiliation: | 1School of Geography And Tourism,Chongqing Normal University,Chongqing401331,China; (2 College of Urban and EnvironmentalSciences,Northwest University,Xi’an710127,Shaanxi,China; 3 School ofArchitectural and Artistic Design,Henan Polytechnic University,Jiaozuo454000,Henan,China |
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| Abstract: | To study the sustainablelivelihood is significant for poverty alleviation and the development of ruralarea. Based on the sustainable livelihood framework, 484 farmer householders were investigated in ShangluoCity of Qinling-Daba mountainousarea, Shaanxi Province, China. The survey data wasused to analyze household livelihood capital between different groups byconstructing the indexes system of household’s livelihood assets, then the impact of livelihood capital on thelivelihood strategies as well as their coupling coordinative degree werediscussed using the Multinomial Logit regression model and the livelihoodcapital coupling coordinative degree model respectively. The results showedas follows: (1) The livelihood strategies of rural householdswere obviously different. According to theproportion of non-agricultural income, there were four types of household livelihoods, namely, exclusively employedby others (Type A),employed by others most of the time (Type B),work part timefor others (Type C) and 100% doing the farm work (Type D). (2) In the surveysamples, the livelihoodcapital of farmers was limited and unbalanced, which presented the characteristics of relatively highfinancial capital and social capital and relatively low natural capital andhuman resources capital. Among the non-poor households, the total livelihood capital of the Type C was thehighest (0.451), followed by the TypeB (0.393) and the Type A (0.382), the Type D (0.215)was the lowest. While among the poorhouseholds, the Type B (0.348)was the highest, followed by the TypeA (0.345) and the Type C (0.342),the Type D (0.184) was the lowest. (3) Theimpact of livelihood capital on the livelihood strategy choice of non-poor households and poor households was different. For the non-poor households, the per capita cultivated land, the per capita forest land area, the cultivated land quality, vocational skill level, political resources and the employment network had apositive effect on the transformation from the Type A to the Type B and theType C, but per capita household income and male labor ratiohad a negative effect. The per capitahousehold income and vocational skill level had a negative effect on thetransformation from the Type A to the Type D. For the poor households, the transformation from the Type A to the Type B, Type C and Type D was positively impacted by the percapita cultivated land, the per capita forestland area and political resources, and the per capita household income, labor education level, vocational skill level and communication expenditurewere negative factors.(4) If lining the coupling coordinative degree of non-poor households' livelihood capital up in order, from the largest to the smallest we have the list asfollows: the Type C (0.114),the Type B (0.106),the Type A (0.103),and the TypeD (0.045).Similarly for the coupling coordinative degree of poor households’ livelihood capital we have the list as follows: theType C (0.095),the Type A (0.094),the Type B (0.092) and the Type D(0.086).This study could provide useful information for the optimization oflivelihood strategies and effective poverty alleviation. |
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| Keywords: | household livelihood capital livelihood strategy Qinling-Daba mountainous area |
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