Grassland conservation programs, vegetation rehabilitation and spatial dependency in Inner Mongolia, China

Three nationwide grassland conservation programs have been implemented in Inner Mongolia since 2000. All aim to relieve grazing pressure, hence to reverse the grassland degradation trend. The different timings and spatial configurations of these programs present an unusual setting of quasi-natural policy experiment for exploring their effectiveness and interactions with other drivers on a regional scale. In this paper, a spatial panel model was developed to examine the effects of the programs on vegetation rehabilitation, meanwhile to detect the spatial interdependent relationship among grassland management units occurring in the process of program implementation. The methodology used a panel dataset of SPOT-VEGETATION NDVI data, multi-station surface meteorological observations, and socio-economic statistics across 88 counties from 2000 to 2013. The modeling results suggested that these programs in general significantly facilitated grassland vegetation rehabilitation. Enrollment in the Beijing-Tianjin Wind/Sand Source Control Program and in the Grazing Withdrawal Program was predicted to increase the normalized difference vegetation index (NDVI) value by an amount equivalent to the effects of 136 mm and 56 mm additional annual precipitation, respectively. The positive and significant coefficient of spatial lag term indicated that there was a synergistic relationship in the vegetation variations of neighboring counties, and a unit increase in the weighted sum of all neighboring counties' NDVI values could approximately increase a target county's NDVI value by 0.2, after controlling for other factors' effects. Certain spatial spillover mechanisms may function to generate this effect, such as benign competition, mutual cooperation and coordination, or sharing of successful experiences among neighboring counties in carrying out the programs. Nevertheless, the actual mechanisms need to be confirmed by field surveys in future studies. (C) 2017 Elsevier Ltd. All rights reserved.