Purpose: As one of its major strategies, China has made a new plan to further expand High Standard Farmland (HSF) to all permanent basic farmland (80% of total farmland) for grain security over the next decade. Yet, what will be the impact of farmland infrastructure investment on agrifood systems? The paper aims to systematically evaluate the multiple effects (food security, economy, nutrition and environment) of expanding HSF construction under the context of the “Big Food vision” using an interdisciplinary model.Design/methodology/approach: An interdisciplinary model – AgriFood Systems Model, which links the China CGE model to diet and carbon emission modules, is applied to assess the multiple effects of HSF construction on agrifood systems, such as food security and economic development, residents’ diet quality and carbon emissions. Several policy scenarios are designed to capture these effects of the past HSF investment based on counterfactual analysis and compare the effects of HSF future investment at the national level under the conditions of different land use policies – restricting to grain crops or allowing diversification (like vegetables, and fruit).Findings: The investments in HSF offer a promising solution for addressing the challenges of food and nutrition security, economic development and environmental sustainability. Without HSF construction, grain production and self-sufficiency would decline significantly, while the agricultural and agrifood systems’ GDP would decrease. The future investment in the HSF construction will further increase both grain production and GDP, improve dietary quality and reduce carbon emissions. Compared with the policy of limiting HSF to planting grains, diversified planting can provide a more profitable economic return, improve dietary quality and reduce carbon emissions.Originality/value: This study contributes to better informing the impact of land infrastructure expanding investment on the agrifood systems from multiple dimensions based on an interdisciplinary model. We suggest that the government consider applying diversified planting in the future HSF investment to meet nutritional and health demands, increase household income and reduce carbon emissions.

Millions of geo-tagged photos are becoming available due to the wide spread of photo-sharing websites, which provide valuable information to mine spatial patterns from human activities. In this study, we present a simple and fast density-based spatial clustering algorithm to detect popular scenic spots using geo-tagged photos collected from Flickr. In this algorithm, Gaussian kernel is applied to estimate local density of data points, and a decision graph is used to obtain cluster centers easily. More than 289,000 geo-tagged photos located in five typical cities of China are downloaded as case studies, and data pre-processing such as duplicate removing is performed to improve the quality of clustering result. Finally, popular tourist attractions of each sample city are successfully detected with this algorithm, and our result is useful for recommending some interesting destinations which might not be on the list of tourist website or mobile guide applications. The proposed solution is robust with respect to different distributions of photos, and it is efficient by comparing with other popular clustering approaches.

Editorial: Agricultural land and environmental risks: evidences, assessment and conservation transition