The non-farming use of cropland has led to food insecurity in China due to drastic land use (LU) changes under the stresses of ecological restoration and urbanization, particularly in non-major grain-producing areas. Questions were raised about spatiotemporal cropland losses/gains and their drivers in these areas in the future for sustainable development of the agriculture sector. However, the answers to these questions have not been well acknowledged. This study, therefore, presents analyses of cropland area change from 1990 to 2018 and from 2018 to 2051 in Shaanxi province based on the Future Land Use Simulation (FLUS) model that follows the integration of the Shared Socioeconomic Pathway 2 and the Representative Concentration Pathway 4.5 (SSP245) within the International Coupled Model Intercomparison Project 6 (CMIP6). The results highlight that ecological restoration and fast-paced urbanization mainly drove the alarming non-farming use of cropland. The per capita cropland area is projected to increase, but the cropland loss will still occur, which potentially causes food insecurity. Thus, food security will be a challenging issue in the near future. The quantitative findings call for careful designs of LU policies, taking into account cropland protection, socio-economic development, and ecological restoration.

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Context Comprehensive understanding of future landscape connectivity change fundamentally benefits both policy-making of land use and ecosystem conservation planning, but such understanding is rarely available at a local level. Here, we present the scenario projections of land use of a crucial ecological barrier, Inner Mongolia (IM) in China, under the interacted frameworks of the shared socio-economic pathways (SSPs) and the Representative Concentration Pathways (RCPs). Objectives We aim to tackle existing issue of future landscape connectivity dynamics with delicate account. Methods We first projected a 1km gridded land use under SSP1-RCP2.6 and SSP5-RCP8.5 covering 2030 and 2050. Probability Connectivity Index (PC) based on Morphological Spatial Pattern Analysis were used to assess landscape connectivity. Aggregation Index (AI) and Shannon’s Diversity Index (SHDI) were used to evaluate landscape pattern and the core patch was identified as three importance levels using Patch Importance Index (dPC), which facilitates bivariate spatial autocorrelation between landscape pattern and dPC. Results The analysts of spatial-temporal landscape connectivity dynamics under two alternative scenarios demonstrate that (1) SSP585 obtains higher landscape connectivity due to larger vegetation coverage with a corresponding homogeneous landscape pattern. (2) Urban expansion is a decisive driver in damaging landscape connectivity. (3) Western region in IM had a worrying situation of landscape connectivity. (4) Higher fragmentation either from urbanization or vegetation occupation damages landscape connectivity. (5) SSP126 obtained higher aggregation effects between AI (or SHDI) and dPC. Conclusion Our results demonstrations call for land use policy interventions geared towards a greener future with high landscape connectivity by reducing cropland loss and grassland loss, preventing damages to landscape connectivity from extensive urban expansion. Especially for the western region, from the perspective of landscape pattern and vegetation distribution, long-term feasible land use spatial planning is formulated.