The Impacts of Farmland Loss on Regional Food Self-Sufficiency in Yangtze River Delta Urban Agglomeration over Last Two Decades

Food security is essential for human survival and sustainable development. Due to rapid urbanization and industrialization, the farmland loss in Yangtze River Delta Urban Agglomeration (YRDUA) has threatened food security. Thus, this study intended to quantify the farmland loss and assess its impacts on food security in the YRDUA from 2000 to 2020 at three scales based on the Google Earth Engine platform. Our results show that the area of farmland in YRDUA is decreasing at a rather high speed in the past 20 years and the trend is getting worse. At the urban agglomeration scale, there was a farmland deficit of 0.44 million ha in 2000, followed by larger farmland deficits in 2010 and 2020. At the city scale, Shanghai had the largest scarcity of farmland. At the urban subgroup scale, Subgroup I in the west and Subgroup II in the north always maintained an oversupply of farmland, while Subgroup III in the east and Subgroup IV in the south faced serious food security problems. Our study suggests that farmland must be protected in YRDUA in order to ensure food self-sufficiency and promote regional sustainability.

Ecosystem Services Changes on Farmland in Response to Urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area of China

Extensive urbanization around the world has caused a great loss of farmland, which significantly impacts the ecosystem services provided by farmland. This study investigated the farmland loss due to urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China from 1980 to 2018 based on multiperiod datasets from the Land Use and Land Cover of China databases. Then, we calculated ecosystem service values (ESVs) of farmland using valuation methods to estimate the ecosystem service variations caused by urbanization in the study area. The results showed that 3711.3 km2 of farmland disappeared because of urbanization, and paddy fields suffered much higher losses than dry farmland. Most of the farmland was converted to urban residential land from 1980 to 2018. In the past 38 years, the ESV of farmland decreased by 5036.7 million yuan due to urbanization, with the highest loss of 2177.5 million yuan from 2000–2010. The hydrological regulation, food production and gas regulation of farmland decreased the most due to urbanization. The top five cities that had the largest total ESV loss of farmland caused by urbanization were Guangzhou, Dongguan, Foshan, Shenzhen and Huizhou. This study revealed that urbanization has increasingly become the dominant reason for farmland loss in the GBA. Our study suggests that governments should increase the construction of ecological cities and attractive countryside to protect farmland and improve the regional ESV.

Power in Policy: Measuring Farmland Loss in Ontario and Testing the Strength of the Greenbelt Act

Farmland in Ontario continues to be under immense pressure from development associated with population growth and urbanization, such as residential subdivisions, commercial developments and aggregate operations. This OMAFRA funded research investigates the strength of the Greenbelt Act in preserving Southern Ontario’s prime agricultural soils by measuring the rate of farmland lost to non-farm land uses from 2000—2017 through official plan amendments. This methodology was applied to 15 counties and regions in southern Ontario and the results provide an assessment on the Greenbelt policy in place. This presentation will report on phase one of this research project (completed) including trends and results from specific jurisdictions and then provide an update on phase two, which extends across the province and is currently underway.

A New Framework for Modelling and Monitoring the Conversion of Cultivated Land to Built-up Land Based on a Hierarchical Hidden Semi-Markov Model Using Satellite Image Time Series

Large amounts of farmland loss caused by urban expansion has been a severe global environmental problem. Therefore, monitoring urban encroachment upon farmland is a global issue. In this study, we propose a novel framework for modelling and monitoring the conversion of cultivated land to built-up land using a satellite image time series (SITS). The land-cover change process is modelled by a two-level hierarchical hidden semi-Markov model, which is composed of two Markov chains with hierarchical relationships. The upper chain represents annual land-cover dynamics, and the lower chain encodes the vegetation phenological patterns of each land-cover type. This kind of architecture enables us to represent the multilevel semantic information of SITS at different time scales. Specifically, intra-annual series reflect phenological differences and inter-annual series reflect land-cover dynamics. In this way, we can take advantage of the temporal information contained in the entire time series as well as the prior knowledge of land cover conversion to identify where and when changes occur. As a case study, we applied the proposed method for mapping annual, long-term urban-induced farmland loss from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series in the Jing-Jin-Tang district, China from 2001 to 2010. The accuracy assessment showed that the proposed method was accurate for detecting conversions from cultivated land to built-up land, with the overall accuracy of 97.72% in the spatial domain and the temporal accuracy of 74.60%. The experimental results demonstrated the superiority of the proposed method in comparison with other state-of-the-art algorithms. In addition, the spatial-temporal patterns of urban expansion revealed in this study are consistent with the findings of previous studies, which also confirms the effectiveness of the proposed method.