China
China
To restore China’s degraded environment and improve ecological balance, the government launched a land use policy known as the Grain for Green Program (GFGP), which is the largest ecological construction project in China or anywhere else. It has greatly increased the vegetation cover on the Loess Plateau but occupied the space of agricultural development (AD) over the years. Research on the impact of policy implementation on ecosystem services (ESs) and their trade-offs can support scenario analyses that provide realistic guidance for decision makers to formulate future regional ecological restoration planning. Our focus is Ningwu County and Jingle County on the Loess Plateau, a typical ecologically fragile and impoverished area where expansion of vegetation cover has driven extensive loss of farmland and its benefits to farmers. In this study, soil conservation (SC), water yield (WY), habitat quality (HQ), and food supply (FS) were selected to assess the change of ESs and their trade-offs in different scenarios that represent ecological restoration and land reclamation policies. The results revealed that overall ESs had the largest increase but there were more trade-offs among ESs under the GFGP scenario. For a single ESs, SC and HQ were increased while FS and WY were decreased under the GFGP scenario. From the view of trade-offs, the GFGP scenario exhibited more trade-offs than the AD scenario. Furthermore, compared to the GFGP scenario, the numbers of sub-watersheds with trade-offs of ESs decreased under the AD scenario. With more concern about the contradiction between implementing GFGP and protecting farmland, we confirm that it is extremely unwise to incessantly implement GFGP to improve overall ESs without considering the trade-offs among ESs. Given the certain sub-watersheds mainly developing agriculture, the AD scenario appears to best reduce trade-offs among ESs and ensure the improvement of ESs. Therefore, it is of great significance to realize the win-win situation of regional ecological and resource demand to find the most suitable development direction in the future.
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