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Acta Prataculturae Sinica ›› 2019, Vol. 28 ›› Issue (4): 12-22.DOI: 10.11686/cyxb2018249

• Orignal Article • Previous Articles     Next Articles

Satisfaction level, and factors influencing satisfaction of herdsmen with the grassland ecological protection subsidy incentive policy

DING Wen-qiang1, 2, YANG Zheng-rong1, 2, MA Chi1, 2, LI Xi-liang2, YIN Yan-ting2, HOU Xiang-yang2, *   

  1. 1.College of Grassland Agricultural Science and Technology, Lanzhou University, Lanzhou 730000, China;
    2.Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010000, China
  • Received:2018-04-23 Revised:2018-08-09 Online:2019-04-20 Published:2019-04-20

Abstract: This study used a multivariate ordered logistic regression model to examine satisfaction with the Grassland Ecological Protection Subsidy Incentive policy, and factors influencing satisfaction for 632 herdsmen in the meadow steppe, typical steppe, desert steppe, sandy steppe and desert regions in Inner Mongolia. It was found that: 1) The herdsmen were generally well satisfied with the policy; as indicated by an average score of 2.61 on a 1-3 scale. 2) There were regional differences in satisfaction score. The regional rankings for satisfaction were: sandy steppe>meadow steppe>desert steppe>desert>typical steppe. 3) Satisfaction with the policy tended to be higher when the household head was older, when farmers owned higher numbers of livestock, or when farmers exercised their grassland management policy transfer right, to move from the grass-livestock balance incentive scheme to the grassland ecological protection subsidy incentive scheme. 4) There were no significant demographic differences among the herdsmen who were very satisfied, generally satisfied or unsatisfied with the policy. However, satisfaction was negatively correlated with the area of grassland owned by the herdsmen, and positively correlated with the level of support received under the policy.

Key words: balance between forage and animal, subsidy and incentive policy, satisfaction, multiple orderly Logistic regression