A look at food security in China

Cui, Kai; Shoemaker, Sharon P.

doi:10.1038/s41538-018-0012-x

Cited by 108 publications

(66 citation statements)

References 1 publication

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“…This suggests that losses in farmland to urban land expansion may continue apace if urban population densities in the country continue to approach levels seen in more developed regions. The loss of farmlands to urban expansion in China, a country concerned with food self-sufficiency [62,63], is driven by the overreliance of city governments on revenue from leasing stateowned lands [64]. Our findings reflect this preferential loss of agricultural lands to urban expansion in the country [65,66].…”

Section: What Types Of Land Covers Are Lost To Urban Expansion?mentioning

confidence: 56%

Trends in urban land expansion, density, and land transitions from 1970 to 2010: a global synthesis

Güneralp

Reba

Hales

et al. 2020

Environ. Res. Lett.

283

102

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The physical expansion of urban areas lead to lasting impacts on landscapes and livelihoods. Here, we conduct a global synthesis of trends in urban land expansion, in urban population densities, and lands converted into urban from 1970 to 2010. We find that small-medium urban areas lead their larger counterparts in both rates of urban land expansion and decreases in urban population densities. Urban population densities have consistently declined only in India, China, North America, and Europe with significant exceptions across city sizes. Over 60% of the reported urban expansion was formerly agricultural land with China, Southeast Asia, and Europe in the lead. Counterfactual analysis suggests that, due to the decrease in urban population densities, an estimated 125 000 km 2 land was converted to urban land uses that could have otherwise remained in cultivation or as natural vegetation. In particular, in India and Nigeria, with much of their populations dependent on agriculture, 85% and 30% more land, respectively, was converted to urban land due to decreasing urban population densities. With increasing urbanization, proactive management of urban land expansion, especially in small and medium cities, will be critical for saving agricultural lands in periurban regions while creating equitable and affordable urban landscapes.

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Section: What Types Of Land Covers Are Lost To Urban Expansion?mentioning

confidence: 56%

Trends in urban land expansion, density, and land transitions from 1970 to 2010: a global synthesis

Güneralp

Reba

Hales

et al. 2020

Environ. Res. Lett.

283

102

View full text Add to dashboard Cite

show abstract

“…In the course of one of them, a quota system was introduced (1955-1993) followed by a land contract reform (1981) that was implemented. Total cereal production increased by 74%, from 354 million tons in 1982 to 618 million tons by 2017, which exceeded the rate of population growth [74]. Currently, China feeds 20% of the global population on 7% of the total agricultural land.…”

Section: China's Food Supplymentioning

confidence: 99%

The Challenge of Feeding the World

2019

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The aim of the present research is to provide a comprehensive review about the current challenges related to food security and hidden hunger. Issues are presented according to major factors, such as growing population, changing dietary habits, water efficiency, climate change and volatile food prices. These factors were compiled from reports of major international organizations and from relevant scientific articles on the subject. Collecting the results and presenting them in an accessible manner may provide new insight for interested parties. Accessibility of data is extremely important, since food security and its drivers form a closely interconnected but extremely complex network, which requires coordinated problem solving to resolve issues. According to the results, the demand for growing agricultural products has been partly met by increasing cultivated land in recent decades. At the same time, there is serious competition for existing agricultural areas, which further limits the extension of agricultural land in addition to the natural constraints of land availability. Agricultural production needs to expand faster than population growth without further damage to the environment. The driving force behind development is sustainable intensive farming, which means the more effective utilization of agricultural land and water resources. Current global trends in food consumption are unsustainable, analyzed in terms of either public health, environmental impacts or socio-economic costs. The growing population should strive for sustainable food consumption, as social, environmental and health impacts are very important in this respect as well. To this end, the benefits of consuming foods that are less harmful to the environment during production are also to be emphasized in the scope of consumption policy and education related to nutrition as opposed to other food types, the production of which causes a major demand for raw materials.

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“…The demand for agricultural production is increasing all over the world as a result of the growing global population. The food consumed annually by China alone has increased significantly over the past decades [1]. China has one of the largest populations in the world, and it is currently undergoing rapid urbanization.…”

Section: Introductionmentioning

confidence: 99%

Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method

Yang

et al. 2019

Remote Sensing

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The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information extraction includes feature classification, which is a long-standing research issue in the RS community. Emerging deep learning techniques, such as the deep semantic segmentation network technique, are effective methods to automatically discover relevant contextual features and get better image classification results. In this study, we exploited deep semantic segmentation networks to classify and extract CA from high-resolution RS images. WorldView-2 (WV-2) images with only Red-Green-Blue (RGB) bands were used to confirm the effectiveness of the proposed semantic classification framework for information extraction and the CA mapping task. Specifically, we used the deep learning framework TensorFlow to construct a platform for sampling, training, testing, and classifying to extract and map CA on the basis of DeepLabv3+. By leveraging per-pixel and random sample point accuracy evaluation methods, we conclude that the proposed approach can efficiently obtain acceptable accuracy (Overall Accuracy = 95%, Kappa = 0.90) of CA classification in the study area, and the approach performs better than other deep semantic segmentation networks (U-Net/PspNet/SegNet/DeepLabv2) and traditional machine learning methods, such as Maximum Likelihood (ML), Support Vector Machine (SVM), and RF (Random Forest). Furthermore, the proposed approach is highly scalable for the variety of crop types in a crop area. Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images.

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A look at food security in China

Cited by 108 publications

References 1 publication

Trends in urban land expansion, density, and land transitions from 1970 to 2010: a global synthesis

Trends in urban land expansion, density, and land transitions from 1970 to 2010: a global synthesis

The Challenge of Feeding the World

Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method

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