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Journal of Geographical Sciences >

2018 , Vol. 28 >Issue 11: 1580 - 1594

DOI: https://doi.org/10.1007/s11442-018-1530-9

Special Issue: Land system dynamics: Pattern and process

Cultivated land change in the Belt and Road Initiative region

  • CHEN Di , 1 ,
  • YU Qiangyi , 1, * ,
  • HU Qiong 1 ,
  • XIANG Mingtao 1 ,
  • ZHOU Qingbo 1 ,
  • WU Wenbin , 1, *
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  • Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*Corresponding author: Yu Qiangyi (1986-), Research Assistant, E-mail: ; Wu Wenbin (1976-), Professor, E-mail:

Author: Chen Di (1991-), PhD Candidate, specialized in agricultural land use change. E-mail:

Received date: 2018-01-04

  Accepted date: 2018-06-06

  Online published: 2018-11-20

Supported by

National Natural Science Foundation of China, No.41501111

Fundamental Research Funds for Central Non-profit Scientific Institution, No.IARRP-2017-27, No.IARRP-2017-65

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

The Belt and Road Initiative (BRI)-a development strategy proposed by China - provides unprecedented opportunities for multi-dimensional communication and cooperation across Asia, Africa and Europe. In this study, we analyse the spatio-temporal changes in cultivated land in the BRI countries (64 in total) to better understand the land use status of China along with its periphery for targeting specific collaboration. We apply FAO statistics and GlobeLand30 (the world’s finest land cover data at a 30-m resolution), and develop three indicator groups (namely quantity, conversion, and utilization degree) for the analysis. The results show that cultivated land area in the BRI region increased 3.73×104 km2 between 2000 and 2010. The increased cultivated land was mainly found in Central and Eastern Europe and Southeast Asia, while the decreased cultivated land was mostly concentrated in China. Russia ranks first with an increase of 1.59×104 km2 cultivated land area, followed by Hungary (0.66×104 km2) and India (0.57×104 km2). China decreased 1.95×104 km2 cultivated land area, followed by Bangladesh (-0.22×104 km2) and Thailand (-0.22×104 km2). Cultivated land was mainly transferred to/from forest, grassland, artificial surfaces and bare land, and transfer types in different regions have different characteristics: while large amount of cultivated land in China was converted to artificial surfaces, considerable forest was converted to cultivated land in Southeast Asia. The increase of multi-cropping index dominated the region except the Central and Eastern Europe, while the increase of fragmentation index was prevailing in the region except for a few South Asian countries. Our results indicate that the negative consequence of cultivated land loss in China might be underestimated by the domestic-focused studies, as none of its close neighbours experienced such obvious cultivated land losses. Nevertheless, the increased cultivated land area in Southeast Asia and the extensive cultivated land use in Ukraine and Russia imply that the regional food production would be greatly improved if China’ “Go Out policy” would help those countries to intensify their cultivated land use.

Key words： spatio-temporal change; land conversion; intensification; multi-cropping; fragmentation; GlobeLand30

Cite this article

CHEN Di , YU Qiangyi , HU Qiong , XIANG Mingtao , ZHOU Qingbo , WU Wenbin . Cultivated land change in the Belt and Road Initiative region[J]. Journal of Geographical Sciences, 2018 , 28(11) : 1580 -1594 . DOI: 10.1007/s11442-018-1530-9

1 Introduction

Historically, China established connections with the world through two geographic Silk Roads. One was a terrestrial Silk Road, which extends along West and Central Asia and finally into Europe, forming the Eurasian integration; while the other was a maritime Silk Road, which connects China to the rest part of Asia as well as to the African continent (Chen et al., 2015). These connections symbolized multi-dimensional communication and cooperation between the East and the West for centuries. In face of the weak recovery of the global economy, China proposes the “Belt and Road Initiative” (BRI) in the early 2010s to revive the Silk Road Spirit (He et al., 2016), aiming to bring “peace and cooperation, openness and inclusiveness, mutual learning and mutual benefit” among all the Belt and Road countries (Gong et al., 2015; Li et al., 2015; Zou et al., 2015).
Many studies have been carried out since the implementation of BRI (Liu et al., 2016), focusing on the investment of energy (Duan et al., 2017), manufacturing (Cheng et al., 2016), water resource (Li et al., 2017), agricultural trade (Gong et al., 2015; Li et al., 2015; He et al., 2016), financial cooperation (Huang, 2016), telecoupling prosperity and ecological sustainability (Yang et al., 2016). However, agricultural land use activities (Foley, 2005) - the foundation of agricultural trade and economic cooperation - have received less attention yet. As a vital resource for food supplies (Foley et al., 2011; Zhao, 2012), it is of great significance to understand the spatio-temporal dynamics of cultivated land and agricultural practices in the BRI countries so as to ensure both domestic and cross-national food security (He et al., 2016). Moreover, understanding the status of cultivated land use in the BRI countries would help China to promote its “Go Out policy” on one hand, and to target specific collaboration for satisfying its global resource demand on the other hand (Wu et al., 2017).
Cultivated land change has been observed in many BRI countries, such as China (Liu et al., 2014), India (Meiyappan et al., 2017), Uzbekistan (Dubovky et al., 2013), and Bangladesh (Islam et al., 2016), for supporting domestic policy-making, yet analyses across countries are rare. Previous global- or continental-level observations of land cover change were primarily based on either statistical data (Ramankutty et al., 2008) or land cover products (Fritz et al., 2015). However, these mostly used land cover dataset are at a relatively coarse spatial resolution, e.g. 300 m or 1 km (Grekousis et al., 2015), resulting in the low cartographic accuracy in the area of fragmentized agricultural landscape and the insufficient details in describing agricultural land use activities. Furthermore, there are multi-faceted characteristics of cultivated land change, e.g. quantity of land cover (He et al., 2017), landscape pattern (Yan et al., 2016), as well as land use intensity (Gray et al., 2014). Previous analyses tend to focus on one aspect while leaving the rest unassessed. As a result, a more inclusive and detailed analysis of cultivated land change across countries within the BRI is imperative for comparison and subsequently for collaboration.
The latest 30 m global land cover data product (GlobeLand30) are recently available (Chen et al., 2014; Chen et al., 2015), which provides the cultivated land maps in 2000 and 2010 with an overall accuracy of 92.82% and 93.13% respectively (Cao et al., 2016). GlobeLand30 shows great potential in characterizing land cover change for a relatively large geographical coverage, given its high spatial resolution and dual observations within a decade (Zhang et al., 2015; Kühling et al., 2015). Consequently, we select GlobeLand30 in addition to FAO statistics for the current study. We develop three indicator groups namely quantity, conversion, and utilization degree, to capture the changing characteristics of cultivated land use pattern in the BRI countries. Based on the core results, we further discuss how China could better implement its “Go Out policy” to meet its global demand for resource collaboration, through the cross-national collaboration on cultivated land use.

2 Material and methods

2.1 Study area

The BRI is an open concept without any fixed area extent, and its coverage is still expanding. Following previous analyses, e.g. Gong et al. (2015) and Zou et al. (2015), we focus on 64 countries from seven geographical zones, including 2 in East Asia, 5 in Central Asia, 11 in Southeast Asia, 7 in South Asia, 20 in Central and Eastern Europe, 19 in West Asia and 1 in North Africa (Table S1 from the Online Supplementary Material).
Both of the “Belt” and “Road” start from China, while the “Silk Road Economic Belt” ends at Europe via Central Asia and West Asia, and the “21st Century Maritime Silk Road” ends at Europe via Southeast Asia, South Asia and North Africa. We therefore use “the Belt” to simplify the “Silk Road Economic Belt”, and use “the Road” to represent the “21st Century Maritime Silk Road” (Figure 1). A map distinguishing the “Belt” and “Road” countries is presented in Figure 1. The selected countries are further linked to the seven geographical zones, as presented in Table S1.
Figure 1 An overview of the Belt and Road Initiative
Due to a higher proportion of developing country in the BRI, agriculture plays an important role in the region’s socio-economic development. For example, the agriculture-accounted national economy (in share) is higher than the global average level (FAOSTAT). However, there are clear disparities in population, resource abundance, technology advancement, labor productivity and agricultural investment among individual countries (Li et al., 2016). Therefore, it would have great implications to focus on the agriculture-related issue in the BRI, especially for the purposes of collaboration.

2.2 Data and application

The essential land cover data of the analysis, i.e. GlobeLand30, is obtained from http://www. globeland30.com. As the finest global land cover dataset at a 30m spatial resolution, GlobeLand30 was produced by using a plenty of medium-high spatial resolution remotely sensed images, including Landsat TM/ETM+ and HJ-1 (Chen et al., 2014; Chen et al., 2015). GlobeLand30 provides two land cover maps of 2000 and 2010 respectively, which were subjected to 10 land cover classes, namely cultivated land, grasslands, forest, shrub land, wetland, water bodies, artificial surfaces, tundra, bare land, and permanent snow and ice. In addition to the global level overall accuracy, the overall accuracy of cultivated land maps in the study area over the study period ranges from 93.63% to 98.90% (Cao et al., 2016). The statistical data, i.e. the country-level harvested area, is acquired from the FAOSTAT (http://www.fao.org/faostat/en/#home).
Three groups of indicators were conceptualized to explore the spatio-temporal changes in cultivated land in the BRI between 2000 and 2010. The first group is related to quantity, including the changes in total cultivated land area (ΔCA) and the area changing rate (RCA). ΔCA and RCA are calculated at the national level:
$\Delta CA=C{{A}_{2010}}-C{{A}_{2000}}$ (1)
${{R}_{CA}}=\frac{\Delta CA}{C{{A}_{2000}}}\times 100%$ (2)
where CA2010 is the total cultivated land area in 2010, CA2000 is the total cultivated land area in 2000.
The second group describes the cultivated land conversions among other land cover classes with the manifestations of the country-level land conversion matrixes, including: (i) the share of cultivated land converted from other land cover classes, and (ii) the share of cultivated land converted to other land cover classes. They are calculated as:
$Share\_I{{N}_{m}}=\frac{I{{N}_{m}}}{C{{A}_{2010}}}\times 100%$ (3)
where INm is the gross cultivated land area converted from land cover class m in a location between 2000 and 2010.
$Share\_OU{{T}_{n}}=\frac{OU{{T}_{n}}}{C{{A}_{2000}}}\times 100%$ (4)
where OUTn is the gross cultivated land area converted to land cover class n in a location between 2000 and 2010.
In the third group, two indicators including multi-cropping index (MCI) and fragmentation index (FI) are adopted for measuring the utilization degree. MCI is an output-oriented measurement of land use intensity (Erb et al., 2013), which has been widely used in agricultural intensification assessments (Wu et al., 2018; Yu et al., 2018). It is calculated as:
$MC{{I}_{it}}=\frac{H{{A}_{it}}}{C{{A}_{it}}}$ (5)
where HA means harvested area and CA means cultivated land area, respectively. i represents country and t represents time stage.
Although the HA applied in this study is acquired from FAOSTAT, while the CA is aggregated from GlobeLand30, we believe such an inter-dataset application is validated. Because GlobeLand30 has been intensively validated by data producers (Cao et al., 2016) as well as by data users (Lu et al., 2016; Lu et al., 2017). To better illustrate the consistency, a comparison between GlobeLand30 and FAOSTAT is presented in Figure S1 (from the Online Supplementary Material).
FI reflects the connectivity of land use patterns (Forman et al., 1995), which could potentially affect the intensity and efficiency of agricultural production (Coppedge et al., 2001; Yu et al., 2018). It is computed as:
$F{{I}_{jt}}=\frac{N{{P}_{jt}}}{C{{A}_{jt}}}\times 100%$ (6)
where NP means the number of cultivated land patches, which is calculated by FRAGSTATS at both country- and county-level. j represents county. When computing the country-level FI, i will be adopted for replacing j.

3 Results

3.1 Spatio-temporal changes in cultivated land area

The cultivated land area in the BRI increased 3.73×104 km2 (ca. +0.39%) between 2000 and 2010 (see details from Table S2 from the Online Supplementary Material). Cultivated land mainly distributed in the eastern part of East Asia, South Asia, Southeast Asia, northrtn part of Central Asia and West Asia, and southwest of Central and Eastern Europe (Figure 2a, upper part). Cultivated land in northeast India, south Kazakhstan, the Nile Delta in Egypt and the central part of Vietnam increased obviously. By contrast, cultivated land along the Yangtze River Delta in China decreased obviously. Cultivated land change in these hotspots - including southwest Iran and southwest Russia - are highlighted in Figure 2b-2h (lower part). These highlighted patterns generally represent the characteristics of cultivated land change at the geographical zone level.
Figure 2 Cultivated land change in the BRI between 2000 and 2010 (a) and the identified seven hotspots representing the characteristics of cultivated land change at the seven geographical zones, including: northeast India from South Asia (b), central part of Vietnam from Southeast Asia (c), Yangtze River Delta in China from East Asia (d), south Kazakhstan from Central Asia (e), southwest Iran from West Asia (f), southwest Russia from Central and Eastern Europe (g) and Nile Delta in Egypt from North Africa (h). Detailed elaboration of the seven geographical zones can be found in Section 2.1 and Table S1. A unified legend is provided in the upper figure (a).
At the geographical zone level, Central and Eastern Europe had the largest net change area in cultivated land (2.86×104 km2), followed by East Asia (-1.98×104 km2) and then Southeast Asia (1.05×104 km2), while Central Asia had the smallest net change area in cultivated land between 2000 and 2010 (Table S2). In terms of the changing rate, North Africa had the largest growth rate (ca. +8.75%), followed by Southeast Asia (+1.11%) and Central and Eastern Europe (+1.02 %). East Asia was the only region where cultivated land decreased (ca. -0.96%), while the net cultivated land loss in China largely contributed to this decrease.
At the country level, a total of 38 countries had increased cultivated land area, while the rest (25 countries) decreased. The higher increase rates were found in Bhutan (+20.25%), Brunei (+15.83%), Laos (+12.24%), among others. By contrast, the higher decrease rates were found in Israel (-18.92%), Jordan (-11.18 %), and Lebanon (-7.31%) (Table S2). These countries have a small amount of cultivated land, so a small amount of change in area results in relatively high rate of change.

3.2 Conversions between cultivated land and other land use types

Between 2000 and 2010, 96.50% of the cultivated land in the BRI remained unchanged. Nevertheless, there were still notable conversions between cultivated land, forest, grassland, bare land and artificial surfaces, with apparent regional disparities. Regarding the increased cultivated land, 3.57% of the cultivated land in Southeast Asia was converted from forest; 1.03% and 10.26% of the cultivated land in West Asia and North Africa, respectively, were converted from bare land; and 1.76% of the cultivated land in Central and Eastern Europe was converted from grassland. Regarding the decreased cultivated land, 3.51% and 1.44% of the cultivated land in Central Asia and West Asia were converted to grassland; 1.76% of the cultivated land in East Asia was converted to artificial surfaces (Table S3).
New cultivated land converted from grassland and bare land was concentrated in “the Belt”, i.e. in the Central Asia and West Asia. Figure 3 shows that there were 15.05%, 11.83% and 3.85% of cultivated land converted from grassland in Armenia, Azerbaijan and Kazakhstan respectively. In the United Arab Emirates and Oman, there were 10.11% and 9.59% of cultivated land converted from bare land, respectively. On the other hand, new cultivated land converted from forest is concentrated in “the Road” countries, i.e. in the Southeast and South Asia. For example, forest in Bhutan and Laos were transferred to cultivated land at a share of 25.80% and 12.90%, respectively, markedly higher than other land cover classes. Cultivated land converted to artificial surfaces were concentrated in China and some countries in “the Road”, e.g. Bangladesh and Singapore. The shares of these conventions were 4.13%, 3.14% and 1.77%, in Bangladesh, Singapore and China, respectively. At the same time, cultivated land converted to grassland was concentrated in some countries of Central and Eastern Europe and in “the Belt”. For example, cultivated land in Russia and Israel were transferred to grassland at a share of 2.05% and 14.16%, respectively, markedly higher than other land cover classes (Figure 3).
Figure 3 The share of cultivated land converted to other land cover classes in BRI countries (a), and the share of other land cover classes converted to cultivated land in the BRI countries (b) between 2000 and 2010

3.3 Spatio-temporal changes in cultivated land utilization degree

3.3.1 Multi-cropping index
The MCI was 0.82 in the BRI in 2010, with an increase of 0.03 (+4.27%) between 2000 and 2010. A few Southeast Asian countries, such as Malaysia, the Philippines and Brunei had a MCI higher than 2.00. Most countries in the BRI had a MCI between 1.00-2.00. While the lowest MCI (i.e. lower than 1.00) were mainly found in Central and Eastern Europe, West Asia, and Central Asia (Figure 4).
Figure 4 Spatial distribution of multi-cropping index and its changing rate in the BRI countries between 2000 and 2010. The number, i.e. 2.9 in the legend, means the length of the presented bar equalizes to this specific value.
The MCI in the southeast of the BRI presented a higher growth rate than it in the northwest of the BRI between 2000 and 2010 (Figure 4), while the growth rate in Southeast Asia was 20.40%, much higher than the rate in West Asia, which was 2.21%. Only Central and Eastern Europe showed a negative growth, which was -13.44%. Specifically, almost all counties in “the Road” region experienced an increase in MCI. For example, Indonesia increased 24.42%, followed by Laos (+23.36%) and Thailand (+15.37%). These countries are clustered in Southeast Asia. Conversely, the MCI change in “the Belt” region displayed a markedly variation. For example, Israel and Kazakhstan increased 21.05% and 22.71%, respectively; whereas Oman and Turkey decreased 19.38% and 9.60% respectively. These countries are clustered in West Asia (Figure 4). China’s MCI increased 6.01%, ranking 29th among the BRI countries (Table S4 from the Online Supplementary Material).
3.3.2 Fragmentation index
The countries in East Asia, Southeast Asia, south of West Asia and some small countries in the south of Central and Eastern Europe had a relatively higher FI, such as China, Philippines, Oman, United Arab Emirates and Albania. On the other hand, the countries in South Asia, Central Asia, West Asia, such as India, Pakistan, Turkmenistan, Iraq and Syria had a relatively lower FI (see details from Figure S2 from the Online Supplementary Material). The FI within counties had larger spatial differences. For example, we find more fragmented cultivated land in south China (Figure S3).
The overall FI in the BRI countries increased 6.51% between 2000 and 2010. It shows a more obvious increase in the Central and Eastern Europe (+23.41%), yet in the Central Asia, South Asia, a decreased trend in FI was found, with a rate of -14.28% and -16.67% respectively (see Table S4 and Figure 5a). At the country scale, half of “the Belt” countries, including Israel, Kazakhstan, Saudi Arabia, had experienced a decrease in the cultivated land FI. The rates were -21.72%, -17.35% and -1.40% respectively. The same trend has been observed in two thirds of “the Road” countries, such as the Philippines (-8.32%), Myanmar (-8.26%), Vietnam (-7.46%). Coincidently, the countries which located at the start and end of “the Belt” and “the Road”, including China, Russia, Belarus, Ukraine, had an increased FI since 2000. In particular, China’s FI increased 2.17% between 2000 and 2010, ranking 33rd among the BRI countries (Table S4). The changes within countries were varied as well. For example, in China, cultivated land became more fragmented in the Yangtze River Delta and Beijing-Tianjin-Hebei Region, but concentrated in parts of south China (Figure 5b).
Figure 5 Changing rate of cultivated land fragmentation index in the BRI countries between 2000 and 2010 at country level (a) and county level (b)

4 Discussion

Between 2000 and 2010, net cultivated land decrease had only been observed in East Asia, where China had contributed to 98.98% of this net loss. The shrink of cultivated land in China was closely related to its urban expansion. It is reported that the newly expanded urban area between 2000 and 2010 was 3.76×104 km2 (Liu et al., 2014), as twice as the amount in the previous decade (Wang et al., 2012; Du et al., 2014). Our results show that 3.62×104 km2 artificial land had been converted from cultivated land, which is close to the previous observations. In addition to urbanization, our study shows that 2.50×104 km2 cultivated land had been converted to forest, which accounts for the second source of cultivated land loss. However, Liu et al. (2014) showed that only 0.24×104 km2 cultivated land had been converted to forest. It is documented that the national level afforestation policy, known as “Green for Grain Project” that started from 1999, had afforested 9.27×104 km2 from cultivated land (Wu et al., 2009). It indicates that more uncertainty exists when we observe the conversions between cultivated land and forest, although our result stands in the middle. In addition to the cross-study comparison, our analysis provides opportunities for inter-country comparison as well. Although it has been acknowledged that cultivated land loss in China is noticeable and would bring adverse consequences on food security and environmental sustainability (Wu et al., 2014), the inter-country comparison suggests that such an issue is more severe than what we used to think, as none of its close neighbors experienced such obvious cultivated land losses. Therefore, it is hoped to stimulate stricter cultivated land protection policy in China.
Small and scattered land patches would probably result in landscape fragmentation. Consequently, the cultivated land FI as measured in our study might potentially relate to the field size of cropland as measured by Fritz et al. (2015). Four regions with relatively high FI are selected and compared with their field size, correspondingly. We could see a trend that locations with a higher FI are likely to have a smaller field size, which supports our hypothesis (Figure 6). However, such a qualitative comparison only stands at the visualization level, because the concepts, data, and methods are totally different from each other. Although Fritz et al. (2015) developed the world’s first field size map that provides a potential for renovating the traditional agricultural land system studies, the results were largely relied on a crowdsourced campaign, which inevitably resulted in a coarse interpretation and limited reliability. Future studies in mapping global field size are anticipated to integrate various data and approaches to improve the accuracy and usability. The cultivated land FI derived from GlobeLand30 would be one of the potential contributors for this interest.
Figure 6 Comparison between fragmentation index and field size in hotspots from China (a, 1 and 2), Southeast Asia (b, 1 and 2), West Asia (c, 1 and 2), Central and Eastern Europe (d, 1 and 2)
Cultivated land fragmentation is not efficient for commercial agriculture, as the costs for production, including labor, machinery, and management would increase dramatically due to the scattered land patches (Lu et al., 2011). However, it shows potential advantages for smallholder agriculture, as it enables diversified planting of crops with limited investment, which would reduce the risk of agricultural production in turn. The BRI is dominated by developing countries, and there used to be prevailing smallholder agriculture (Loayza et al., 2010). Our analysis shows that the FI in many countries decreased, particularly in Southeast Asia. Those regions are likely experiencing drastic agriculture transformation, as shown by the less fragmented landscape. Large-scale land acquisition - the buying or leasing of large pieces of land for commercial agriculture - has been emerging as a global phenomenon since the 2000s, and has been intensively documented in Southeast Asia (Arezki et al., 2011; Chen et al., 2017). This transformation is partly reflected by our result as it captures the changes in cultivated land concentration in Laos and Vietnam. However, in contrast to Southeast Asia, FI has increased in some parts of China, Central and Eastern Europe, and West Asia. Noticeable cultivated land loss is also observed in these regions, e.g. to artificial land, grassland, and bare land, respectively. This implies that urbanization (Liu et al., 2009; Liu et al., 2014), land abandonment (Estel et al., 2015; Meyfroidt et al., 2016) and social conflicts (Yang et al., 2009) played an even more important role in landscape change than agriculture transformation. The fragmented land tenure (i.e. use right) originated from the Household Responsibility System policy underlines the cultivated land fragmentation in China (Li et al., 2003). Although China hopes to promote agricultural modernization through consolidating the land use right (Yu et al., 2013), cultivated land loss due to urbanization has driven the landscape even more fragmented, which would pose negative effects on realizing this goal.
Agricultural cooperation is anticipated between China and the BRI countries through cultivated land use activities. One alternative is to optimize the use of cultivated land resource. Our study shows a prevailing conversion between cultivated land and grassland in Russia, Kazakhstan and Ukraine, confirming that cultivated land abandonment has substantially happened in Central and Eastern Europe (Meyfroidt et al., 2016). On the other hand, this region would have large amount of potential cropland resources, as confirmed by Lambin et al. (2013) and Eitelberg et al. (2015). It is reported that China has invested 3.00×104 km2 abandoned cultivated land in Ukraine. The “win-win” consequence is emerging through such cooperation: the agricultural investment from China provides necessary financial and technical assistance to Ukraine, while the food produced in Ukraine contributes to Chinese food security in turn (Ba, 2013). Russia had the world’s largest chernozem (Li et al., 2015), and Kazakhstan had abundant grassland resources (Zhang et al., 2015), however, both of these countries are lack of population and their MCI are relatively low. As Ukraine is becoming China’s largest overseas agricultural production base, similar model could be duplicated to these countries.
Another alternative is to share the advanced experience in cultivated land management. Our study shows that cultivated land area in Israel also decreased a lot (ca. -18.92%), but with an increase in MCI (ca. +21.05%) and a decrease in fragmentation (ca. -21.72%). As Israel is famous for its irrigation technology (Tal et al., 2016), the forementioned result suggests that agricultural production is not only determined by cultivated land quantity, which could also be improved by advanced agricultural technologies (Wu et al., 2014). Israel’s agricultural practices shed light on cultivated land management in China, especially for its vast western regions where agricultural productivity is relatively low due to the arid climate (similar to Israel). On the other hand, China’s agricultural achievements, especially in its main breadbaskets (e.g. Northeast China, North China, and Lower Yangtze Plains), are worthy for improving cultivated land management in its adjacent Southeast Asia and South Asian countries, where cultivated land has increased significantly after the global food crisis in 2008 (Hong et al., 2008, Sudaryanto et al., 2009), yet the crop yield gap (Neumann et al., 2010) and the cropping intensity gap, e.g. due to the relative low MCI against a relative high MCI potential (Wu et al., 2018), are still largely existing. The optimized use of cultivated land resource and the sharing of advanced cultivated land management would sustain the China’s agriculture “Go Out”, and would eventually contribute to the sustainable agricultural development within the BRI.
It needs to be noted that this study cannot extend to a long period given that the GlobeLand30 contains only two land cover maps of 2000 and 2010. An amount of comparative studies show that GlobeLand30 has a higher accuracy than other global land cover datasets such as UMD-GLC, GlobCover2009, BU-MODIS, ESACCI (Liu et al., 2015; Lu et al., 2016; Chen et al., 2017). The different data sources, definitions, classification schemes and methods, bring much inconsistency between these global land cover datasets, in particular in those regions with fragmented landscapes and small sizes of cultivated land (Cao et al., 2016). It is thus not reasonable to combine these datasets for change studies as the heterogeneity of these datasets is far bigger than the changes of the real world. Extending the mapping period of GlobeLand30 or developing the latest global land cover map at GlobeLand30 or even higher resolution is of great importance for future global-scale land cover change analysis.

5 Conclusions

Between 2000 and 2010, cultivated land area in the BRI increased 3.73×104 km2. Russia ranks first with an increase of 1.59×104 km2 cultivated land area, which is followed by Hungary (0.66×104 km2) and India (0.57×104 km2). China ranks first with a decrease of 1.95×104 km2 cultivated land area, following by Bangladesh (-0.22×104 km2) and Thailand (-0.22×104 km2). In total, there are 25 countries having cultivated land area decreased, comparing to 38 countries which have cultivated land area increased. Our analysis reveals that the total cultivated land area in the BRI increased slightly, which is the results of a noticeable cultivated land area loss in China in combination of an overall cultivated land increase in the other BRI countries. This implies that the negative consequence of cultivated land loss in China might be underestimated by the domestic-focused studies, as none of its close neighbors experienced such obvious cultivated land losses. Moreover, the land change matrix indicates that the cultivated land loss in China is mainly associated with the increases in artificial land, while the cultivated land increases in Southeast Asia and Central and Eastern Europe are mainly converted from forest and grassland. It implies that these regions - which experienced cultivated land increase - would have huge potential to increase food production, despite the cultivated lands are largely fragmented and extensively managed there. Given the challenges brought by cultivated land loss in China, not only a more rigorous domestic policy is needed to prevent further cultivated land loss due to urbanization, but also effective international collaborations are anticipated to optimize the use of cultivated land resources at the regional level. A potential solution would be strengthening the financial and technical assistance between China and the countries in Central and Eastern Europe and Southeast Asia to improve their food production through a more intensified cultivated land use management.

Supplementary Material

Table S1 Countries in the “Belt and Road Initiative” (BRI) region
BRI Geographical zone Countries
China (the start) East Asia China
the Belt East Asia Mongolia
Central Asia Kazakhstan, Turkmenistan, Tajikistan, Uzbekistan, Kyrgyzstan
West Asia Armenia, Syrian, Lebanese, Afghanistan, Iraq, Kuwait, Jordan, Bahrain, Qatar, Georgia, Israel, Iran, Yemen, Saudi Arabia, United Arab Emirates, Turkey, Oman, Azerbaijan
the Road Southeast Asia Brunei, East Timor, Laos, Singapore, Cambodia, Myanmar, Thailand, Indonesia, Malaysia, Vietnam, Philippines
South Asia Maldives, Sri Lanka, Pakistan, India, Bangladesh, Nepal, Bhutan
North Africa Egypt
Central and
Eastern Europe
(the end)		 Central and Eastern Europe Estonia, Latvia, Lithuania, Belarus, Poland, Czech, Slovakia, Moldova, Hungary, Slovenia, Romania, Serbia, Ukraine, Bosnia and Herzegovina, Croatia, Bulgaria, Macedonia, Albania, Russia, Montenegro
Table S2 Cultivated land area change (104 km2) and changing rate (%) of seven geographical zones and each country in the BRI region between 2000 and 2010
Geographical zone Country Area change Area changing rate
East Asia China -1.95 -0.95
Mongolia -0.03 -2.00
Subtotal -1.98 -0.96
Central Asia Kazakhstan 0.17 0.40
Turkmenistan 0.17 4.87
Tajikistan 0.02 1.86
Uzbekistan -0.06 -0.89
Kyrgyzstan 0.02 0.92
Subtotal 0.31 0.55
West Asia Armenia 0.0001 0.02
Syrian -0.003 -0.04
Lebanese -0.02 -7.31
Afghanistan 0.21 3.57
Iraq -0.001 -0.01
Kuwait 0.001 2.39
Jordan -0.05 -11.18
Geographical zone Country Area change Area changing rate
Bahrain -0.00003 -0.96
Qatar 0.00005 0.35
Georgia -0.01 -0.58
Israel -0.12 -18.92
Iran 0.32 1.40
Yemen -0.04 -3.16
Saudi Arabia 0.08 2.97
United Arab Emirates 0.01 6.44
Turkey -0.04 -0.15
Oman 0.02 10.50
Azerbaijan 0.10 3.20
Subtotal 0.44 0.52
Southeast Asia Brunei 0.0006 15.83
East Timor 0.0007 1.78
Laos 0.24 12.24
Singapore 0.0006 5.07
Cambodia 0.28 4.78
Myanmar 0.10 0.64
Thailand -0.22 -0.82
Indonesia 0.42 1.67
Malaysia 0.01 0.49
Vietnam 0.21 1.68
Philippines 0.003 0.05
Subtotal 1.05 1.11
South Asia Maldives 0.00 0.00
Sri Lanka 0.06 3.28
Pakistan 0.27 1.01
India 0.57 0.29
Bangladesh -0.22 -2.53
Nepal -0.01 -0.25
Bhutan 0.02 20.25
Subtotal 0.69 0.29
North Africa Egypt 0.35 8.75
Subtotal 0.35 8.75
Central and Eastern Europe Estonia 0.003 0.18
Latvia -0.03 -1.03
Lithuania -0.02 -0.47
Belarus -0.18 1.72
Poland -0.08 -0.39
Geographical zone Country Area change Area changing rate
Czech -0.003 -0.06
Slovakia -0.01 -0.42
Moldova 0.003 0.12
Hungary 0.66 11.69
Slovenia -0.004 -0.59
Romania 0.39 2.98
Serbia 0.01 0.13
Ukraine 0.50 1.25
Bosnia and Herzegovina -0.01 -0.45
Croatia 0.01 0.58
Bulgaria 0.02 0.35
Macedonia -0.001 -0.15
Albania 0.004 0.55
Russia 1.59 1.02
Montenegro -0.0004 -0.15
Subtotal 2.86 1.02
The BRI region Total 3.73 0.39
Table S3 Share (%) of cultivated land converted into other land cover types, and other land cover types converted into cultivated land in the BRI region between 2000 and 2010
Geographical zone Country Cultivated land converted OUT Cultivated land converted IN
(to) forest (to) grassland (to) artificial surfaces (to)
bare land		 (from) forest (from) grassland (from) artificial surfaces (from) bare land
East Asia China 1.22 1.41 1.77 0.04 1.33 1.28 0.72 0.20
Mongolia 0.04 5.21 0.02 0.01 0.08 3.22 0.01 0.04
Subtotal 1.22 1.43 1.76 0.04 1.32 1.30 0.72 0.20
Central Asia Kazakhstan 0.15 3.83 0.09 0.02 0.30 3.85 0.03 0.19
Turkmenistan 0.02 4.05 0.61 0.50 0.13 3.16 0.65 5.32
Tajikistan 0.04 2.86 1.40 0.28 0.10 3.44 1.18 1.31
Uzbekistan 0.01 2.12 1.61 0.24 0.10 1.44 0.96 0.50
Kyrgyzstan 0.01 0.85 0.56 0.05 0.13 1.62 0.13 0.37
Subtotal 0.12 3.51 0.36 0.08 0.25 3.42 0.22 0.58
West Asia Armenia 0.31 1.91 0.76 0.00 3.83 15.05 0.87 0.07
Syrian 0.00 0.12 0.02 0.00 0.07 0.02 0.01 0.00
Lebanese 0.97 8.80 0.51 0.19 0.97 1.50 0.80 0.16
Afghanistan 0.01 3.31 0.16 1.03 0.11 2.66 0.10 4.66
Iraq 0.02 0.05 0.02 0.11 0.01 0.03 0.02 0.10
Kuwait 0.01 0.09 1.48 5.08 0.09 0.41 0.63 7.69
Jordan 0.66 9.03 0.44 5.65 0.27 1.28 0.67 2.89
Bahrain 0.00 0.00 1.93 0.00 0.00 0.00 0.99 0.00
Geographical zone Country Cultivated land converted OUT Cultivated land converted IN
(to) forest (to) grassland (to) artificial surfaces (to)
bare land		 (from) forest (from) grassland (from) artificial surfaces (from) bare land
Qatar 0.05 0.35 0.25 3.43 0.02 0.20 0.04 4.05
Georgia 0.73 2.24 1.13 0.00 4.84 4.25 0.92 0.00
Israel 4.28 14.16 1.34 3.92 0.55 1.10 2.59 1.20
Iran 0.06 1.67 0.22 0.85 0.14 1.80 0.16 1.34
Yemen 0.10 1.17 0.08 2.93 0.10 0.11 0.15 1.08
Saudi Arabia 0.01 0.09 0.50 4.75 0.06 0.36 0.23 7.26
United Arab
Emirates		 0.04 0.00 0.85 3.98 0.14 0.02 0.24 10.11
Turkey 0.55 1.17 0.20 0.00 0.49 1.12 0.11 0.00
Oman 0.00 0.00 1.69 1.06 1.60 0.00 0.60 9.59
Azerbaijan 0.22 2.40 0.70 0.00 1.61 11.83 0.71 0.02
Subtotal 0.28 1.44 0.24 0.59 0.43 1.74 0.18 1.03
Southeast Asia Brunei 4.78 0.06 0.09 0.00 7.52 0.45 9.95 0.00
East Timor 1.05 8.61 0.00 7.17 10.52 6.46 0.82 0.43
Laos 5.20 0.78 0.17 0.00 12.90 3.26 0.05 0.01
Singapore 0.86 0.04 3.14 0.00 7.52 0.45 9.95 0.00
Cambodia 0.24 0.06 0.16 0.00 3.50 0.51 0.17 0.00
Myanmar 3.13 0.65 0.68 0.02 3.61 1.58 0.24 0.06
Thailand 2.58 0.10 0.47 0.00 1.72 0.39 0.14 0.00
Indonesia 3.07 0.29 0.42 0.00 4.89 0.27 0.46 0.00
Malaysia 3.45 0.13 0.83 0.00 4.17 0.13 0.50 0.00
Vietnam 2.96 0.97 1.04 0.01 4.18 1.56 0.70 0.10
Philippines 0.98 0.11 0.22 0.02 1.12 0.18 0.19 0.01
Subtotal 2.67 0.37 0.54 0.01 3.57 0.76 0.32 0.02
South Asia Maldives 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Sri Lanka 4.46 0.03 0.18 0.00 5.48 1.76 0.32 0.09
Pakistan 0.14 0.23 0.13 0.16 0.21 0.53 0.11 0.72
India 0.30 0.29 0.16 0.09 0.49 0.31 0.11 0.08
Bangladesh 1.89 0.13 4.13 0.05 0.30 0.15 2.64 0.25
Nepal 1.26 0.19 0.05 0.01 0.99 0.12 0.05 0.08
Bhutan 8.48 3.57 0.00 0.00 25.80 0.65 0.09 2.36
Subtotal 0.39 0.27 0.30 0.09 0.51 0.33 0.20 0.16
North Africa Egypt 0.07 0.02 1.29 2.21 0.03 0.01 0.84 10.26
Subtotal 0.07 0.02 1.29 2.21 0.03 0.01 0.84 10.26
Central and Eastern Europe Estonia 1.89 0.48 0.46 0.00 2.05 0.20 0.50 0.00
Latvia 3.07 0.10 0.36 0.00 1.74 0.03 0.37 0.00
Lithuania 2.15 0.08 0.67 0.00 1.45 0.03 0.65 0.00
Belarus 3.62 1.32 1.01 0.00 1.50 0.97 1.06 0.00
Geographical zone Country Cultivated land converted OUT Cultivated land converted IN
(to) forest (to) grassland (to) artificial surfaces (to)
bare land		 (from) forest (from) grassland (from) artificial surfaces (from) bare land
Poland 1.09 0.16 0.69 0.00 0.95 0.04 0.40 0.00
Czech 1.74 0.05 0.77 0.00 1.62 0.08 0.69 0.00
Slovakia 0.80 0.01 0.75 0.00 0.45 0.01 0.65 0.00
Moldova 0.65 0.73 1.14 0.00 0.33 0.85 1.43 0.00
Hungary 0.71 0.03 0.49 0.01 0.66 0.16 0.39 0.00
Slovenia 4.76 0.02 1.13 0.01 4.23 0.03 0.81 0.01
Romania 1.00 0.13 0.91 0.01 1.00 0.12 0.96 0.02
Serbia 1.04 0.05 0.44 0.00 0.99 0.29 0.37 0.00
Ukraine 0.89 0.74 0.74 0.00 0.61 1.83 0.84 0.01
Bosnia and
Herzegovina		 3.11 0.06 0.50 0.01 2.65 0.07 0.39 0.01
Croatia 1.35 0.02 0.61 0.04 1.47 0.02 0.58 0.04
Bulgaria 0.97 0.13 0.75 0.01 1.13 0.22 0.78 0.01
Macedonia 1.45 0.05 0.53 0.00 1.54 0.05 0.35 0.00
Albania 1.20 0.73 0.48 0.01 1.42 0.86 0.58 0.03
Russia 1.10 2.05 0.34 0.00 1.23 2.57 0.32 0.02
Montenegro 2.28 0.19 0.64 0.00 2.38 0.23 0.40 0.00
Subtotal 1.22 1.33 0.52 0.00 1.13 1.76 0.50 0.01
The Belt region Subtotal 0.21 2.27 0.28 0.39 0.35 2.41 0.19 0.85
The Road region Subtotal 1.04 0.30 0.38 0.09 1.38 0.45 0.25 0.25
The BRI region Total 1.01 1.13 0.70 0.10 1.14 1.30 0.41 0.26
Table S4 Changing rate (%) in multi-cropping index, and fragmentation index of seven geographical zones and each country in the BRI region between 2000 and 2010
Geographical zone Country MCI changing rate FI changing rate
East Asia China 6.01 2.17
Mongolia 27.77 -2.82
Subtotal 6.06 2.16
Central Asia Kazakhstan 22.71 -17.35
Turkmenistan -2.76 -8.55
Tajikistan 0.62 15.53
Uzbekistan 1.45 14.44
Kyrgyzstan -3.21 -2.20
Subtotal 14.72 -14.28
West Asia Armenia 4.11 -0.34
Syrian 6.40 -0.97
Lebanese -5.49 3.82
Afghanistan 20.06 2.13
Geographical zone Country MCI changing rate FI changing rate
Iraq -1.76 0.95
Kuwait 87.13 19.41
Jordan 32.04 -21.81
Bahrain 36.26 13.58
Qatar -29.94 0.00
Georgia -45.10 1.62
Israel 21.05 -21.72
Iran 18.88 -0.28
Yemen 41.95 -49.59
Saudi Arabia -30.48 -1.40
United Arab Emirates -11.03 36.70
Turkey -9.60 34.67
Oman -19.38 3.42
Azerbaijan 19.35 -1.89
Subtotal 2.21 4.19
Southeast Asia Brunei -25.74 22.88
East Timor 20.44 -1.78
Laos 23.36 -16.52
Singapore 49.09 -40.20
Cambodia 54.40 -2.88
Myanmar 39.15 -8.26
Thailand 15.37 1.09
Indonesia 24.42 52.22
Malaysia 9.30 -6.67
Vietnam 7.55 -7.46
Philippines 9.39 -8.32
Subtotal 20.40 10.47
South Asia Maldives 0.00 0.00
Sri Lanka 7.03 -12.32
Pakistan 2.13 3.66
India 8.82 -30.71
Bangladesh 8.16 10.11
Nepal 7.44 -0.57
Bhutan -22.18 129.89
Subtotal 7.98 -16.67
North Africa Egypt 2.35 -5.91
Subtotal 2.35 -5.91
Central and Eastern Europe Estonia -24.44 9.85
Latvia 16.04 58.77
Lithuania -6.02 69.96
Geographical zone Country MCI changing rate FI changing rate
Belarus -9.55 3.59
Poland -7.45 133.17
Czech -16.51 89.39
Slovakia -11.25 6.66
Moldova -11.01 75.19
Hungary -20.58 -9.42
Slovenia 0.79 17.63
Romania -4.76 60.83
Serbia 93.08 28.88
Ukraine 3.38 77.19
Bosnia and Herzegovina 2.53 38.88
Croatia -24.36 74.29
Bulgaria 2.76 113.41
Macedonia -15.55 34.42
Albania 24.88 63.98
Russia -24.33 6.40
Montenegro -28.60 89.90
Subtotal -13.44 23.41
The BRI region Total 4.27 6.51
Figure S1 Comparison between the cultivated land area of GlobeLand30 and FAOSTAT in 2000 (a) and 2010 (b)
Figure S2 Fragmentation index of cultivated land in 2000 (a) and 2010 (b) in the BRI region at a country level
Figure S3 Fragmentation index of cultivated land in 2000 (a) and 2010 (b) in the BRI region at a county level

Acknowledgement

We thank the Agricultural Land System group at AGRIRS that provided valuable support throughout the research.

The authors have declared that no competing interests exist.

References

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DOI

[21]
Gong B, Song Z, Liu W, 2015. Commodity structure of trade between China and countries in the Belt and Road Initiative area.Progress in Geography, 30(5): 571-580. (in Chinese)Economic cooperation is one of the priority areas in the "Belt and Road Initiative" proposed by China.It is important to examine the characteristics and patterns of development of trade between China and countries in the "Belt and Road Initiative" area, for achieving "unimpeded trade" and promoting economic prosperity and regional cooperation. Under this background, this article reviews the changing trend of commodity structure of trade between China and countries in the "Belt and Road Initiative" area, and analyzes the commodity structure and pattern, based on the revealed comparative advantage index(RCA), sensitive industry identification method,and k-medium value clustering. The results show that the commodity structure of China's export to these countries has improved, while import has been more centralized with increasing share of energy. Second, the main products that China exports to these countries are mechanical equipment and textiles and garments, while the main products that China imports are mostly energy, textiles and garments, and mechanical equipment. Third,sensitive industries involved in China's exports include clothing and shoes, nonmetallic minerals, transportation equipment and so on, and those involved in China's imports are mainly ores, energy, and some primary processed products. Fourth, at the provincial level, eastern, central, and some western provinces that do not share border with other countries, are mainly connected to Southeast Asia, West Asia, and Middle East, while western and northern border provinces are mainly dependent on trade with neighboring countries in the area, and have more ties with Central Asia, South Asia, and Mongolia-Russia. Most eastern provinces, central provinces, and several fast-growing western provinces are mainly exporting mechanical equipment, while most northwestern provinces are mainly exporting clothing. On the other hand, energy is the main product imported to Qinghai, Xinjiang, Liaoning, Heilongjiang, and other eastern provinces, while ores and metal products are main imported to most western provinces.

[22]
Gray J, Friedl M, Frolking S et al., 2014. Mapping Asian cropping intensity with MODIS.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(8): 3373-3379.Agricultural systems are geographically extensive, have profound significance to society, and affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, there is a growing pressure to increase yields on existing agricultural lands. In tropical and subtropical regions, multicropping is widely used to increase food production, but regional-to-global information related to multicropping practices is poor. The high temporal resolution and moderate spatial resolution of the MODIS sensors provide an ideal source of information for characterizing cropping practices over large areas. Relative to studies that document agricultural extensification, however, systematic assessment of agricultural intensification via multicropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multitemporal remote sensing to map multicropping systems in Asia. Image time-series analysis is especially challenging in this part of the world because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low-quality observations, especially during the Asian Monsoon. The methodology that we developed builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but uses an improved methodology optimized for crops. We assessed our results at the aggregate scale using state, district, and provincial level inventory statistics reporting total cropped and harvested areas, and at the field scale using survey results for 191 field sites in Bangladesh. While the algorithm highlighted the dominant continental-scale patterns in agricultural practices throughout Asia, and produced reasonable estimates of state and provincial level total harvested areas, field-scale assessment revealed significant challenges in mapping high cropping intensity due to abundant missing data.

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[23]
Grekousis G, Mountrakis G, Kavouras M, 2015. An overview of 21 global and 43 regional land-cover mapping products.International Journal of Remote Sensing, 36(21): 5309-5335.Land-cover (LC) products, especially at the regional and global scales, comprise essential data for a wide range of environmental studies affecting biodiversity, climate, and human health. This review builds on previous compartmentalized efforts by summarizing 23 global and 41 regional LC products. Characteristics related to spatial resolution, overall accuracy, time of data acquisition, sensor used, classification scheme and method, support for LC change detection, download location, and key corresponding references are provided. Operational limitations and uncertainties are discussed, mostly as a result of different original modelling outcomes. Upcoming products are presented and future prospects towards increasing usability of different LC products are offered. Despite the common realization of product usage by non-experts, the remote-sensing community has not fully addressed the challenge. Algorithmic development for the effective representation of inherent product limitations to facilitate proper usage by non-experts is necessary. Further emphasis should be placed on international coordination and harmonization initiatives for compatible LC product generation. We expect the applicability of current and future LC products to increase, especially as our environmental understanding increases through multi-temporal studies.

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[24]
He M, Huang Z, Zhang N, 2016. An empirical research on agricultural trade between China and “The Belt and Road” countries: Competitiveness and complementarity.Modern Economy, 7: 1671-1686.

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[25]
He Y, Lee E, Warner T A, 2017. A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data.Remote Sensing of Environment, 199: 201-217.

DOI

[26]
Hong K, 2008. The impact of global food crisis on Southeast Asian countries and China’s countermeasures. Southeast Asian Studies,(6): 31-35, 84. (in Chinese)

[27]
Huang Y, 2016. Understanding China’s Belt & Road Initiative: Motivation, framework and assessment.China Economic Review, 40: 314-321.

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[28]
Islam M, Miah M, Inoue Y, 2016. Analysis of land use and land cover changes in the coastal area of Bangladesh using Landsat imagery.Land Degradation & Development, 27(4): 899-909.Coastal land use across the globe has experienced remarkable rapid change over the recent decades because of extraordinary anthropogenic pressure and climate variability and change. Therefore, quantitative information about coastal land use change is imperative for effective management and planning resources for sustainable development. We analysed the quantitative land use and land cover changes during 1989–2000–2010 periods in three important agroecological zones of the most vulnerable coastal region of Bangladesh using Landsat images (Thematic Mapper/Enhanced Thematic Mapper Plus). In the Ganges Tidal Floodplain, the area under shrimp cultivation greatly increased at the rate of 2·05% per annum. The majority of the shrimp area gained from conversion of single cropland. In the Meghna Estuarine Floodplain, decreased mudflat and water bodies were observed, which was predominantly converted into cropland. In Chittagong Coastal Plain, salt pan–shrimp area increased with the expense of single and/or double cropland. In all the study areas, settlement area considerably increased over time. The dynamics of land use change have been attributed to low and unstable food production in the coastal region. The approach adopted in study and the results obtained from the study would likely be useful for policy making and identifying direction for future studies on the coastal land use in Bangladesh. Copyright 08 2015 John Wiley & Sons, Ltd.

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[29]
Kühling I, Broll G, Trautz D, 2016. Spatio-temporal analysis of agricultural land-use intensity across the western Siberian grain belt.Science of the Total Environment, 544: 271-280.61We developed a normalized index to quantify agricultural land-use intensity.61An individual input-based index was calculated for cropland and grassland.61Land-use intensity changed significantly across post-Soviet Western Siberia.61Intensification on cropland and a decrease in intensity on grassland was observed.61Sustainable land management demands a different strategy for each land-use type.

DOI PMID

[30]
Lambin E F, Gibbs H K, Ferreira L et al., 2013. Estimating the world’s potentially available cropland using a bottom-up approach.Global Environmental Change, 23(5): 892-901.Previous estimates of the land area available for future cropland expansion relied on global-scale climate, soil and terrain data. They did not include a range of constraints and tradeoffs associated with land conversion. As a result, estimates of the global land reserve have been high. Here we adjust these estimates for the aforementioned constraints and tradeoffs. We define potentially available cropland as the moderately to highly productive land that could be used in the coming years for rainfed farming, with low to moderate capital investments, and that is not under intact mature forests, legally protected, or already intensively managed. This productive land is underutilized rather than unused as it has ecological or social functions. We also define potentially available cropland that accounts for trade-offs between gains in agricultural production and losses in ecosystem and social services from intensified agriculture, to include only the potentially available cropland that would entail low ecological and social costs with conversion to cropland. In contrast to previous studies, we adopt a ottom-up approach by analyzing detailed, fine scale observations with expert knowledge for six countries or regions that are often assumed to include most of potentially available cropland. We conclude first that there is substantially less potential additional cropland than is generally assumed once constraints and trade offs are taken into account, and secondly that converting land is always associated with significant social and ecological costs. Future expansion of agricultural production will encounter a complex landscape of competing demands and tradeoffs.

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[31]
Li F, Dong S, Yuan L et al., 2016. Study on agriculture patterns and strategy of the Belt and Road.Bulletin of Chinese Academy of Sciences, 31(6): 678-688. (in Chinese)

[32]
Li J, Mancini M, Su B et al., 2017. Monitoring water resources and water use from earth observation in the Belt and Road Countries.Bulletin of the Chinese Academy of Sciences, 32(Suppl.1): 62-73.

[33]
Li X, Wang X, 2003. Changes in agricultural land use in China: 1981-2000.Asian Geographer, 22(1/2): 27-42.Rapid economic development following the 1978 reforms is well reflected in agricultural land use changes in China. By investigating the magnitude of changes in agricultural land use intensity across regions in a geographical perspective, in terms of the intensity indicators defined, explanation for the regional disparity is provided. An important implication is that economic comparative profit drives farmers to increase the intensity level for agricultural production in the western provinces at present but not forever. Lower incentive for raising agricultural land use intensity may more seriously threaten food security than the shrinking farmland area or lower technological potential.

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[34]
Liu J, Kuang W, Zhang Z et al., 2014. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s.Acta Geographica Sinica, 69(1): 3-14. (in Chinese)Land-use/land-cover changes (LUCCs) have links to both human and nature interactions. China's Land-Use/cover Datasets (CLUDs) were updated regularly at 5-year intervals from the late 1980s to 2010,with standard procedures based on Landsat TM\ETM+ images. A land-use dynamic regionalization method was proposed to analyze major land-use conversions. The spatiotemporal characteristics,differences,and causes of land-use changes at a national scale were then examined. The main findings are summarized as follows. Land-use changes (LUCs) across China indicated a significant variation in spatial and temporal characteristics in the last 20 years (1990-2010). The area of cropland change decreased in the south and increased in the north,but the total area remained almost unchanged. The reclaimed cropland was shifted from the northeast to the northwest. The built-up lands expanded rapidly,were mainly distributed in the east,and gradually spread out to central and western China. Woodland decreased first,and then increased,but desert area was the opposite. Grassland continued decreasing. Different spatial patterns of LUC in China were found between the late 20th century and the early 21st century. The original 13 LUC zones were replaced by 15 units with changes of boundaries in some zones. The main spatial characteristics of these changes included (1) an accelerated expansion of built-up land in the Huang-Huai-Hai region,the southeastern coastal areas,the midstream area of the Yangtze River,and the Sichuan Basin;(2) shifted land reclamation in the north from northeast China and eastern Inner Mongolia to the oasis agricultural areas in northwest China;(3) continuous transformation from rain-fed farmlands in northeast China to paddy fields;and (4) effectiveness of the "Grain for Green" project in the southern agricultural-pastoral ecotones of Inner Mongolia,the Loess Plateau,and southwestern mountainous areas. In the last two decades,although climate change in the north affected the change in cropland,policy regulation and economic driving forces were still the primary causes of LUC across China. During the first decade of the 21st century,the anthropogenic factors that drove variations in land-use patterns have shifted the emphasis from one-way land development to both development and conservation.The "dynamic regionalization method" was used to analyze changes in the spatial patterns of zoning boundaries,the internal characteristics of zones,and the growth and decrease of units. The results revealed "the pattern of the change process," namely the process of LUC and regional differences in characteristics at different stages. The growth and decrease of zones during this dynamic LUC zoning,variations in unit boundaries,and the characteristics of change intensities between the former and latter decades were examined. The patterns of alternative transformation between the "pattern" and "process" of land use and the causes for changes in different types and different regions of land use were explored.

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[35]
Liu J, Peng S, Chen J et al., 2015. Knowledge based quality checking method and engineering practice of GlobeLand30 cropland data.Bulletin of Surveying and Mapping, (4): 42-48. (in Chinese)

[36]
Liu J, Zhang Z, Xu X et al., 2009. Spatial patterns and driving forces of land use change in China in the early 21st century.Acta Geographica Sinica, 64(12): 1411-1420. (in Chinese)

[37]
Liu W, Dunford M, 2016. Inclusive globalization: Unpacking China’s Belt and Road Initiative.Area Development and Policy, 1(3): 323-340.Abstract China’s Belt and Road Initiative (BRI) is a call for an open and inclusive (mutually beneficial) model of cooperative economic, political and cultural exchange (globalization) that draws on the deep-seated meanings of the ancient Silk Roads. While it reflects China’s rise as a global power, and its industrial redeployment, increased outward investment and need to diversify energy sources and routes, the BRI involves the establishment of a framework for open cooperation and new multilateral financial instruments designed to lay the infrastructural and industrial foundations to secure and solidify China’s relations with countries along the Silk Roads and to extend the march of modernization and poverty reduction to emerging countries.

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[38]
Loayza N V, Raddatz C, 2010. The composition of growth matters for poverty alleviation.Journal of Development Economics, 93: 137-151.This paper contributes to explain the cross-country heterogeneity of the poverty response to changes in economic growth. It does so by focusing on the structure of output growth itself. The paper presents a two-sector theoretical model that clarifies the mechanism through which the sectoral composition of growth and associated labor intensity can affect workers' wages and, thus, poverty alleviation. Then, it presents cross-country empirical evidence that analyzes, first, the differential poverty-reducing impact of sectoral growth at various levels of disaggregation, and, second, the role of unskilled labor intensity in such differential impact. The paper finds evidence that not only the size of economic growth but also its composition matters for poverty alleviation, with the largest contributions from unskilled labor-intensive sectors (agriculture, construction, and manufacturing). The results are robust to the influence of outliers, endogeneity concerns, alternative explanations, and various poverty measures.

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[39]
Lu M, Wu W, Zhang L et al., 2016. A comparative analysis of five global cropland datasets in China.Science China Earth Sciences, 59(12): 2307-2317.Accurate information of cropland area and spatial location is critical for studies of national food security, global environmental change, terrestrial ecosystem geophysics and the geochemical cycle. In this paper, we compared five global cropland datasets in circa 2010 of China from in terms of cropland area and spatial location, including GlobalLand30, FROM-GLC, GlobCover, MODIS Collection 5, and MODIS Cropland. The results showed that the accuracies of cropland area and spatial location of GlobeLand30 were higher than the other four products. The cropland areas of the five products varied in most of the provinces. Compared with the statistical data, the best goodness of fit was obtained using GlobeLand30, followed by MODIS Collection 5 and FROM-GLC, with MODIS Cropland and GlobCover having the poorer accuracies. Regarding the spatial location of cropland, GlobeLand30 achieved the best accuracy, followed by FROM-GLC and MODIS Collection 5, with GlobCover and MODIS Cropland having the poorer accuracies. In addition, the spatial agreement of the five datasets was reduced from agricultural production area to pastoral area and significantly affected by elevation and slope factors. Although the spatial resolution of MODIS Collection 5 was the lowest, accuracies of the cropland area and spatial location were better than those of GlobCover and MODIS Cropland. Therefore, high spatial resolution remote sensing images can help to improve the accuracy of the dataset during land cover mapping, while it is also important to select a suitable classification method. Furthermore, in northwestern and southeastern China, spectral mixing pixels are universal because of the complicated landscape and fragmentized topography and result in uncertainty and poor consistency when using the five products. Therefore, these regions require additional attention in future cropland mapping studies.

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[40]
Lu M, Wu W, You L et al., 2017. A synergy cropland of China by fusing multiple existing maps and statistics.Sensors, 17(1613): 1-16.

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[41]
Lu X, Huang X, Zhong T et al., 2011. A review of farmland fragmentation in China. Journal of Natural Resources, 26(3): 530-540. (in Chinese)Here,we describe research on farmland fragmentation using the summary and comparison analysis approaches.The definition of farmland fragmentation,main research fields and measurement methods are reviewed.The connotation of farmland fragmentation is clear and has been widely recognized,but methods for determining fragmentation require further work.Farmland fragmentation research in China mainly focuses on the causes and its effect on agricultural production,particularly the negative impacts.The relationship between farmland fragmentation and land consolidation has received increasing attention;the relationship between farmland fragmentation and land transfer less so.Research in this area mainly draws on economic research methods,and geographical spatial analyses are absent.Several suggestions are made,including additional comparative studies across different areas based on different economic and social backgrounds;strengthening research on the relationship between farmland fragmentation and the comprehensive regulation of rural land;and adoption of RS and GIS methods.

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[42]
Meiyappan P, Roy P, Sharma Y et al., 2017. Dynamics and determinants of land change in India: Integrating satellite data with village socioeconomics.Reg. Environ. Change, 17(3): 753-766.We examine the dynamics and spatial determinants of land change in India by integrating decadal land cover maps (1985–1995–2005) from a wall-to-wall analysis of Landsat images with spatiotemporal soci

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[43]
Meyfroidt P, Schierhorn F, Prishchepov A et al., 2016. Drivers, constraints and trade-offs associated with recultivating abandoned cropland in Russia, Ukraine and Kazakhstan.Global Environmental Change, 37: 1-15.Further cropland expansion might be unavoidable to satisfy the growing demand for land-based products and ecosystem services. A crucial issue is thus to assess the trade-offs between social and ecological impacts and the benefits of converting additional land to cropland. In the former Soviet Union countries, where the transition from state-command to market-driven economies resulted in widespread agricultural land abandonment, cropland expansion may incur relatively low costs, especially compared with tropical regions. Our objectives were to quantify the drivers, constraints and trade-offs associated with recultivating abandoned cropland to assess the potentially available cropland in European Russia, western Siberia, Ukraine and Kazakhstan—the region where the vast majority of post-Soviet cropland abandonment took place. Using spatial panel regressions, we characterized the socio-economic determinants of cropland abandonment and recultivation. We then used recent maps of changes in cropland to (i) spatially characterize the socio-economic, accessibility and soil constraints associated with the recultivation of abandoned croplands and (ii) investigate the environmental trade-offs regarding carbon stocks and habitat for biodiversity. Less cropland abandonment and more recultivation after 2000 occurred in areas with an increasing rural population and a younger labor force, but also improved yields. Synergies were observed between cropland recultivation and intensification over the 2000s. From 47.3million hectares (Mha) of cropland abandoned in 2009, we identified only 8.5 (7.1–17.4)Mha of potentially available cropland with low environmental trade-offs and low to moderate socio-economic or accessibility constraints that were located on high-quality soils (Chernozems). These areas represented an annual wheat production potential of 6514.3 (9.6–19.5)million tons (Mt). Conversely, 8.5 (4.2–12.4)Mha had high carbon or biodiversity trade-offs, of which 6510% might be attractive for cropland expansion and thus would require protection from recultivation. Agro-environmental, accessibility, and socio-economic constraints suggested that the remaining 30.6 (25.7–30.6)Mha of abandoned croplands were unlikely to provide important contributions to future crop production at current wheat prices but could provide various ecosystem services, and some could support extensive livestock production. Political and institutional support could foster recultivation by supporting investments in agriculture and rural demographic revitalization. Reclaiming potentially available cropland in the study region could provide a notable contribution to global grain production, with relatively low environmental trade-offs compared with tropical frontiers, but is not a panacea to address global issues of food security or reduce land-use pressure on tropical ecosystems.

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[44]
Neumann K, Verburg P H, Stehfest E et al., 2010. The yield gap of global grain production: A spatial analysis.Agricultural Systems, 103(5): 316-326.Global grain production has increased dramatically during the past 50 years, mainly as a consequence of intensified land management and introduction of new technologies. For the future, a strong increase in grain demand is expected, which may be fulfilled by further agricultural intensification rather than expansion of agricultural area. Little is known, however, about the global potential for intensification and its constraints. In the presented study, we analyze to what extent the available spatially explicit global biophysical and land management-related data are able to explain the yield gap of global grain production. We combined an econometric approach with spatial analysis to explore the maximum attainable yield, yield gap, and efficiencies of wheat, maize, and rice production. Results show that the actual grain yield in some regions is already approximating its maximum possible yields while other regions show large yield gaps and therefore tentative larger potential for intensification. Differences in grain production efficiencies are significantly correlated with irrigation, accessibility, market influence, agricultural labor, and slope. Results of regional analysis show, however, that the individual contribution of these factors to explaining production efficiencies strongly varies between world-regions.

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[45]
Ramankutty N, Evan A T, Monfreda C et al., 2008. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles,(22): GB1003. doi: 10.1029/2007GB002952.1] Agricultural activities have dramatically altered our planet's land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite-derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite-derived products (Boston University's MODIS-derived land cover product and the GLC2000 data set). Our data are presented at 5 min (09080410 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.209000917.1) million km2 of cropland (12% of the Earth's ice-free land surface) and 28.0 (90% confidence range of 23.609000930.0) million km2 of pasture (22%) in the year 2000.

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[46]
Sudaryanto T, 2009. Policy response to the impact of global food crisis in Indonesia.Extension Bulletin - Food & Fertilizer Technology Center, (624): 1-10.Skyrocketing food prices in the world market during 2007-2008 have raised concerns on the ability of the poor to meet sufficient food intake. However, the Indonesian government was able to stabilize domestic food prices and secure the availability of sufficient food stock. This achievement was brought about by sound government policy on food production, distribution, and social safety net progr...

[47]
Tal A, 2016. Rethinking the sustainability of Israel’s irrigation practices in the drylands.Water Research, 90: 387-394.61The sustainability of intensive use of drip irrigation and recycled effluents is assessed.61Drip irrigation remains a critical component for irrigation strategies in drylands.61Research confirms damage to soil and crops from prolonged utilization of recycled wastewater.61Effluent reuse requires highly treated effluents and ultimately desalinization of wastewater.

DOI PMID

[48]
Wang L, Li C, Ying Q et al., 2012. China’s urban expansion from 1990 to 2010 determined with satellite remote sensing.Chinese Science Bulletin, 57(22): 2802-2812.Based on the same data source of Landsat TM/ETM+ in 1990s, 2000s and 2010s, all urban built-up areas in China are mapped mainly by human interpretation. Mapping results were checked and refined by the same analyst with the same set of criteria. The results show during the last 20 years urban areas in China have increased exponentially more than 2 times. The greatest area of urbanization changed from Northeastern provinces in 1990s to the Southeast coast of China in Jiangsu, Guangdong, Shandong, and Zhejiang in 2010s. Urban areas are mostly converted from croplands in China. Approximately 17750 km croplands were converted into urban lands. Furthermore, the conversion from 2000 to 2010 doubled that from 1990 to 2000. During the 20 years, the most urbanized provinces are Jiangsu, Guangdong, Shandong and Zhejiang. We also analyzed built-up areas, gross domestic production (GDP) and population of 147 cities with a population of greater than 500000 in 2009. The result shows coastal cities and resource-based cities are with high economic efficiency per unit of built-up areas, resource-based cities have the highest population density, and the economic efficiency of most coastal provinces are lower than central provinces and Guangdong. The newly created urban expansion dataset is useful in many fields including trend analysis of urbanization in China; simulation of urban development dynamics; analysis of the relationship among urbanization, population growth and migration; studies of carbon emissions and climate change; adaptation of climate change; as well as land use and urban planning and management.

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[49]
Wu F, Zhang H, 2017. China’s Global Quest for Resources: Energy, Food and Water. Oxon, New York: Routledge, 5-6.

[50]
Wu L, Liu Q, Li L, 2009. A review of the progress of the national Green for Grain Project.Forestry Economics, 9: 21-37. doi: 10.13843/j.cnki.lyjj.2009.09.007. (in Chinese)

[51]
Wu W, Verburg P H, Tang H, 2014. Climate change and the food production system: Impacts and adaptation in China.Reg. Environ. Change, 14(1): 1-5.No Abstract available for this article.

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[52]
Wu W, Yu Q, Verburg P H et al., 2014. How could agricultural land systems contribute to raise food production under global change. Journal of Integrative Agriculture, 13(7): 1432-1442.To feed the increasing world population, more food needs to be produced from agricultural land systems. Solutions to produce more food with fewer resources while minimizing adverse environmental and ecological consequences require sustainable agricultural land use practices as supplementary to advanced biotechnology and agronomy. This review paper, from a land system perspective, systematically proposed and analyzed three interactive strategies that could possibly raise future food production under global change. By reviewing the current literatures, we suggest that cropland expansion is less possible amid fierce land competition, and it is likely to do less in increasing food production. Moreover, properly allocating crops in space and time is a practical way to ensure food production. Climate change, dietary shifts, and other socio-economic drivers, which would shape the demand and supply side of food systems, should be taken into consideration during the decision-making on rational land management in respect of sustainable crop choice and allocation. And finally, crop-specific agricultural intensification would play a bigger role in raising future food production either by increasing the yield per unit area of individual crops or by increasing the number of crops sown on a particular area of land. Yet, only when it is done sustainably is this a much more effective strategy to maximize food production by closing yield and harvest gaps.

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[53]
Wu W, Yu Q, You L et al., 2018. Global cropping intensity gaps: Increasing food production without cropland expansion.Land Use Policy, 76: 515-525.To feed the world’s growing population, more food needs to be produced using currently available cropland. In addition to yield increase, increasing cropping intensity may provide another promising opportunity to boost global crop production. However, spatially explicit information on the cropping intensity gap (CIG) of current global croplands is lacking. Here, we developed the first spatially explicit approach to measure the global CIG, which represents the difference between the potential and actual cropping intensity. Results indicate that the global average CIG around the year 2010 was 0.48 and 0.17 for the temperature- and temperature/precipitation-limited scenarios, respectively. Surprisingly, global harvest areas can be expanded by another 7.3662million62 km 2 and 2.7162million62km 2 (37.55% and 13.83% of current global cropland) under the two scenarios, respectively. This will largely compensate the future global cropland loss due to increasing urbanization and industrialization. Latin America has the largest potential to expand its harvest area by closing the CIGs, followed by Asia. Some countries in Africa have a large CIG, meaning that some additional harvests can potentially be achieved. Our analysis suggests that reducing the CIG would provide a potential strategy to increase global food production without cropland expansion, thus also helping achieve other Sustainable Development Goals such as biodiversity conservation and climate change mitigation.

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[54]
Yan L, Roy D P, 2016. Conterminous United States crop field size quantification from multi-temporal Landsat data.Remote Sensing of Environment, 172: 67-86.61First-ever quantitative CONUS crop field size map and histogram61CONUS-wide object extraction from Landsat time series61Over 4.1 million crop fields were extracted automatically.61Validated using pixel and object based accuracy metrics

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[55]
Yang D, Cai J, Hull V et al., 2016. New road for telecoupling global prosperity and ecological sustainability.Ecosystem Health and Sustainability, 2(10): e01242.Abstract Top of page Abstract Introduction Challenges Recommendations Acknowledgments Literature Cited China's ambitious Belt and Road Initiative, which seeks to expand the ancient land routes that connect China to the Mediterranean Sea and corresponding ocean-based routes, is expanding global cooperation with profound socioeconomic and ecological implications. As China and associated countries are developing specific policies to implement the initiative, it is important to analyze and integrate major relevant issues. In this article, we discuss several major challenges facing the Belt and Road region: complex natural features, mismatched resources, shared ecological issues, and diverse socioeconomic conditions. To meet the challenges, we apply the integrated framework of telecoupling (socioeconomic and environmental interactions over distances) and propose to enhance infrastructure connection, transboundary actions, scientific and cultural exchanges, and institutional innovations within the Belt and Road region; and collaborate with more international organizations and countries beyond the Belt and Road region for a prosperous and sustainable world.

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[56]
Yang J, Sun J.2009. Probing into the food security issues in the Arabian countries.West Asia and Africa, (11): 33-40. (in Chinese)

[57]
Yu Q, Hu Q, van Vliet J et al., 2018. GlobeLand30 shows little cropland area loss but greater fragmentation in China.International Journal of Applied Earth Observation and Geoinformation, 66: 37-45.Understanding of cropland dynamics in a large geographical extent is mostly based on observations of area change, while the changes in landscape pattern are hardly assessed. The total amount of cropland in China has remained relatively stable in recent years, which might suggest there was little change. In this analysis, we combine the number of cropland patches (NP) with the total cropland area (TA) for a more comprehensive characterization of cropland change in China. We use GlobeLand30 global land cover dataset with a 30 m resolution for the years 2000 and 2010 nd characterize changes in TA and NP for each county as increase, stable, or decrease. This characterization shows that 703 out of 2420 counties experienced both cropland loss and increased fragmentation. The predominant cropland loss in these areas, especially in the North China Plain, is converted to artificial land. Another 212 are characterized by the opposite developments: an increase in cropland and decreased fragmentation. These counties, are mainly characterized by a conversion of forest areas and grassland areas. It suggests that the cropland conservation policy in China effectively protected the total cropland area in overall, but the consequences in terms of fragmentation might be underestimated. Counties with no obvious change in both indicators, measuring 279 counties, are mainly located in the Southeast. Our results are further compared with local level case studies: the fair consistency indicates alternatives of applying GlobeLand30 for analyzing landscape changes across scales and for cross-site comparisons.

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[58]
Yu Q, van Vliet J, Verburg P H et al., 2018. Harvested area gaps in China between 1981 and 2010: Effects of climatic and land management factors.Environmental Research Letters, 13: 044006.

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[59]
Yu Q, Wu W, Verburg P H et al., 2013. A survey-based exploration of land-system dynamics in an agricultural region of northeast China.Agricultural Systems, 121: 106-116.Understanding the complexity of agricultural systems requires insight into the human–environment interactions. In this paper we used survey data to analyze land system change and its relation to farmer’s attitudes in a typical agricultural region of Northeast China, focusing on land tenure, crop choice and intensification. Our survey shows that land transfer was fairly common across the study area: average farmland acreage per household almost doubled from 1.3ha by early 1980s to 2.6ha by early 2010s, especially due to urban migration of farmers. The survey indicates an increase in land transfers over time with a sharp decrease of the average period of land transfer contracts. Crop choice displays a trend of decreasing diversity as several cereal crops are no longer grown in the study region and the majority of bean cultivation has been replaced by maize and tobacco. Land transfers can explain part of these changes, butnot necessarily the full change to a dominance of a smaller number of crops at the region level. Irrigation intensity is related to the locations of rivers, while agricultural inputs, along with land transfer and crop allocation, show a spatial pattern which is related to road accessibility. Survey results show that two family characteristics (education level and the initially allocated land rights) and two socioeconomic factors (infrastructure and crop prices) are important in making land transfer decisions, while external factors such as market, policy, local cropping system, and agricultural disasters have substantially influenced crop choice decisions. The survey approach is very valuable to analyze land system changes from a stakeholder’s perspective, especially in the absence of statistical data atfarm level.

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[60]
Zhang Y, Chen J, Chen L et al., 2015. Characteristics of land cover change in Siberia based on GlobeLand30, 2000-2010.Progress in Geography, 34(10): 1324-1333. (in Chinese)

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[61]
Zhang Y, Yang G, Yang Y, 2015. The Belt and Road strategy: To strengthen China and Central Asian agricultural cooperation opportunities.Transnational Business, (1): 31-43. (in Chinese)

[62]
Zhao W, 2012. Arable land change dynamics and their driving forces for the major countries of the world.Acta Ecologica Sinica, 32(20): 6452-6462. (in Chinese)Arable land is an essential resource for the production of food and thus constitutes one of the most fundamental resources for mankind.This resource is burdened by population growth and economic development.The statistic data from the Food and Agriculture Organization of the United Nations showed that the world′s arable land area was 1.401 billion hectares in 1990,and dropped to 1.381 billion hectares in 2008;with the continued population growth,the world′s per capita arable land was 0.265 ha in 1990,and dropped to 0.205 ha in 2008.By 2050,the world′s population will reach 9.1 billion.Assuming that the world′s arable land area in 2008 remains unchanged to 2050,the world′s per capita arable land would fall to 0.151 hectares.Having enough arable land to feed the world population in 2050 is a major challenge,and it is a meaningful task to explore the arable land dynamics for the major countries of the world. This paper selected 21 countries for a case study,and the arable land dynamics and their possible driving factors for these countries were discussed.These countries includes ten countries with the largest cultivated areas in the world,and the countries whose population will exceed 1 billion by 2050.The research results show that,from 1961 to 2007,increasingly more countries′ arable land areas were declining,and almost all of the countries are facing the shortage challenges of arable land.In fact,90.5% countries have suffered a downward trend of per capita arable land,which implies that the world food crisis is constantly increasing.Considering the change of total arable land area and per capita arable land area,the 21 countries can be divided into four groups:(1) Total arable land area and per capita arable land area increase at the same time;(2) Total arable land area and per capita arable land area decrease at the same time;(3) Total arable land area increases but per capita arable land area decreases;or(4) Total arable land area decreases but per capita arable land area increases. Among the different situations,population growth and economic development have been two of the key driving forces for the arable land changes.However,due to different land use potentials and different degrees of political stability,the influence factors of arable land are different among the referenced counties.For Brazil,agricultural acreage expansion and the ethanol production increase are important reasons for deforestation and arable land increases.For Bangladesh,Japan,Russia and the United States,urbanization and industrialization are the main reasons behind the reduction of arable land.However,for the Ukraine,the reduced total arable land and increased per capita arable land are closely connected to the sharp population drop and increased urban development.For the other 15 countries,rapid population growth and urbanization lead to reduced per capita arable land;at the same time,population growth has also become an important driving force for these countries to increase the total amount of cultivated land to ensure food security.However,because different countries have different reserve land resources,the arable land growth rate among these countries is significantly different.

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[63]
Zou J, Liu C, Yin G et al., 2015. Spatial patterns and economic effects of China’s trade with countries along the Belt and Road.Progress in Geography, 34(5): 598-605. (in Chinese)Policy coordination, facilities connectivity, unimpeded trade, financial integration, and people-to-people bond are the focus of international cooperation of the "Belt and Road Initiative". Exports of the provinces in China to the "Belt and Road Initiative" area is the main content of the "Unimpeded trade and Financial integration," but research on trade between China and countries in the "Belt and Road Initiative" area are relatively rare,and trade interdependence remains unclear. According to the latest data from the International Trade Center, Chinese customs statistics in 2014, and Multi-regional Input-Output Table of China's 30 provinces in 2010, we analyzed the trade interdependence between China and countries of the "Belt and Road Initiative" area, and the contribution of provincial export to the GDP of each province. The results show that: trade interdependence had deepen between China and countries of the "Belt and Road Initiative" area, but the interdependence was asymmetrical; at the provincial level, the relatively high GDP contribution of exports in coastal provinces shows that these provinces are more export-dependent. Xinjiang has the highest GDP contribution of export(to Central Asia)and is thus strongly export dependent.

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