Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (2): 179-196.doi: 10.1007/s11442-020-1722-y
• Research Articles • Next Articles
CAI Jianming1,2,3, MA Enpu1,2,3, LIN Jing1,*(), LIAO Liuwen1,2,3, HAN Yan1,2,3
Received:
2019-08-02
Accepted:
2019-10-30
Online:
2020-02-25
Published:
2020-02-21
Contact:
LIN Jing
E-mail:linjing@igsnrr.ac.cn
About author:
Cai Jianming (1961-), Professor, specialized in urban and rural sustainable development. E-mail: caijm@igsnrr.ac.cn
Supported by:
CAI Jianming, MA Enpu, LIN Jing, LIAO Liuwen, HAN Yan. Exploring global food security pattern from the perspective of spatio-temporal evolution[J].Journal of Geographical Sciences, 2020, 30(2): 179-196.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
Table 1
Evaluation index system and measurement methods of food security"
Overall index | First layer factors | Second layer indicators | +/- influence | Measurement methods |
---|---|---|---|---|
Food security index | Food supply | X1: per capita food production (kg/person) | +(1) | X1 = total grain production/total population |
X2: per capita protein supply (g/person*day) | + | X2 = supply of food protein/total population * days of the year | ||
X3: per capita animal protein supply (g/person*day) | + | X3 = animal protein supply/total population * days of the year | ||
X4: rate of dietary energy supply (%) | + | X4 = population with daily dietary energy greater than 2320 kcal(2)/total population | ||
Food access | X5: food deficiency (kcal/person/day) | - | X5 = 2320 - daily per capita dietary energy taken by malnourished populations | |
X6: per capita GDP (2011- dollar value) | + | X6 = gross domestic product converted by purchasing power parity/total population | ||
Food utilization | X7: the proportion of short children under 5 (%) | - | X7 = number of short children under 5/number of children under 5 | |
X8: the proportion of wasted children under 5 (%) | - | X8 = number of wasted children under 5/number of children under 5 | ||
X9: proportion of population with access to clean water ( % ) | + | X9 = population with clean water/total population | ||
Economic and political stability | X10: variability in food production per capita | - | X10 = standard deviation of per capita food production/average of per capita food production | |
X11: variability of food supply per capita(kcal/person/day) | - | X11 = standard deviation of per capita food supply | ||
X12: political stability and non-violence level | + | X12: World Governance Indicators Developed by the World Bank (WGI)(3) |
Table 2
The selection of influencing factors of food security and data sources"
Influencing factors | Methods and data | Data resource websites |
---|---|---|
Z1: per capita arable land area (ha/person) | Z1 = arable land area/total population | http://www.fao.org/faostat/en/ |
Z2: per capita renewable water resources (m3/person) | Z2 = renewable water resources/total population | renewable water resource:http://chartsbin.com/view/1469;population:http://www.fao.org/faostat/en/ |
Z3: annual precipitation (mm) | Z3: provided by the climate research unit at the University of East Anglia | https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/crucy.1506241137.v3.23/countries/pre/ |
Z4: annual average temperature (℃) | Z4: provided by the climate research unit at the University of East Anglia | https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/crucy.1506241137.v3.23/countries/tmp/ |
Z5: coordination degree of land and water | Z5 = renewable water/arable land area | renewable water resource:http://chartsbin.com/view/1469;arable land area:http://www.fao.org/faostat/en/ |
Z6: chemical fertilizer applied per unit land area (kg/ha) | Z6 = applied chemical fertilizers/arable land area | http://www.fao.org/faostat/en/ |
Z7: CO2 emissions (kt) | Z7: from the National Greenhouse Gas Emission Dataset of the World Resources Institute. This dataset is a combination of data from Oak Ridge National Laboratory, FAO, International Energy Agency, World Bank, and Environmental Protection Agency. | http://datasets.wri.org/dataset/cait-country |
Z8: per capita GDP (dollar value in 2011) | Z8 = GDP (dollar value in 2011)/total population | http://www.fao.org/faostat/en/ |
Z9: the proportion of the population with access to clean water (%) | Z9 = population with access to clean water/total population | http://www.fao.org/faostat/en/ |
Z10: political stability and non-violence level | Z10: World Governance Indicators Developed by the World Bank | https://datacatalog.worldbank.org/dataset/worldwide-governance-indicators |
Table 4
Moran’s I, z-score and P-value of the food security index from 2000 to 2014"
Year | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Moran's I | 0.22 | 0.22 | 0.22 | 0.23 | 0.24 | 0.25 | 0.28 | 0.27 | 0.28 | 0.26 | 0.29 | 0.25 | 0.27 | 0.24 | 0.27 |
z-score | 14.00 | 13.84 | 13.71 | 14.40 | 15.14 | 15.39 | 17.12 | 16.51 | 17.69 | 16.44 | 18.27 | 15.44 | 16.68 | 15.04 | 17.04 |
P-score | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Table 5
Multi-linear regression equations served as control"
Year | Multi-linear regression equations | R2 | F | Sig. |
---|---|---|---|---|
2002 | FSI=0.26-0.14Z4+0.60Z8+0.42Z9+0.39Z10 | 0.78 | 114.45 | 0.00 |
2003 | FSI=0.22-0.19Z4+0.61Z8+0.49Z9+0.37Z10 | 0.79 | 118.01 | 0.00 |
2004 | FSI=0.19-0.19Z4+0.56Z8+0.52Z9+0.39Z10 | 0.78 | 110.60 | 0.00 |
2005 | FSI=0.06-0.17Z4+0.51Z8+0.62Z9+0.43Z10 | 0.79 | 117.77 | 0.00 |
2006 | FSI=0.31+0.14Z3-0.32Z4+0.71Z8+0.43Z9+0.33Z10 | 0.83 | 124.08 | 0.00 |
2007 | FSI=0.20-0.17Z4-0.36Z6+0.70Z8+0.55Z9+0.34Z10 | 0.83 | 121.30 | 0.00 |
2008 | FSI=0.32-0.24Z4+0.68Z8+0.47Z9+0.28Z10 | 0.82 | 147.35 | 0.00 |
2009 | FSI=0.31+0.11Z3-0.24Z4-0.18Z6+0.76Z8+0.44Z9+0.28Z10 | 0.83 | 102.51 | 0.00 |
2010 | FSI=0.26-0.16Z4-0.45Z6+0.86Z8+0.48Z9+0.24Z10 | 0.83 | 124.83 | 0.00 |
2011 | FSI=0.009-0.10Z4-0.64Z6+0.82Z8+0.61Z9+0.36Z10 | 0.86 | 153.29 | 0.00 |
2012 | FSI=0.23-0.10Z4-0.59Z6+0.83Z8+0.40Z9+0.31Z10 | 0.86 | 152.71 | 0.00 |
2013 | FSI=0.28-0.10Z4+0.35Z8+0.31Z9+0.25Z10 | 0.69 | 70.32 | 0.00 |
2014 | FSI=0.25-0.12Z4+0.39Z8+0.36Z9+0.28Z10 | 0.72 | 80.87 | 0.00 |
Table 6
Transformation equations"
Year | Transformation equations | R2 | F | Sig. |
---|---|---|---|---|
2002 | FSI=-0.24+0.09T4+0.57T8+0.30T9+0.29T10 | 0.83 | 160.09 | 0.00 |
2003 | FSI=-0.23+0.10T4+0.55T8+0.35T9+0.30T10 | 0.83 | 157.89 | 0.00 |
2004 | FSI=-0.28+0.11T4+0.54T8+0.37T9+0.29T10 | 0.82 | 141.20 | 0.00 |
2005 | FSI=-0.23+0.06T4+0.47T8+0.42T9+0.33T10 | 0.82 | 146.46 | 0.00 |
2006 | FSI=-0.23-0.04T3+0.16T4+0.56T8+0.34T9+0.25T10 | 0.88 | 185.76 | 0.00 |
2007 | FSI=-0.22+0.14T4-0.03T6+0.53T8+0.37T9+0.28T10 | 0.84 | 129.99 | 0.00 |
2008 | FSI=0.20T4+0.64T8-0.03T9+0.28T10 | 0.85 | 186.05 | 0.00 |
2009 | FSI=-0.31+0.04T3+0.16T4-0.02T6+0.59T8+0.28T9+0.30T10 | 0.86 | 128.65 | 0.00 |
2010 | FSI=-0.29+0.15T4+0.04T6+0.60T8+0.30T9+0.23T10 | 0.86 | 160.09 | 0.00 |
2011 | FSI=-0.22+0.08T4-0.06T6+0.55T8+0.35T9+0.34T10 | 0.85 | 147.59 | 0.00 |
2012 | FSI=-0.24+0.10T4-0.001T6+0.60T8+0.26T9+0.33T10 | 0.87 | 162.64 | 0.00 |
2013 | FSI=-0.23+0.10T4+0.37T8+0.46T9+0.39T10 | 0.72 | 81.11 | 0.00 |
2014 | FSI=-0.24+0.09T4+0.52T8+0.34T9+0.37T10 | 0.77 | 106.88 | 0.00 |
Table 7
Multi-nonlinear regression equations"
Year | Multi-nonlinear regression equations |
---|---|
2002 | FSI=0.30+0.27Z43-0.41Z42+0.12Z4-1.20Z82+1.52Z8+1.85Z93-2.83Z92+1.35Z9+0.20Z102+0.02Z10 |
2003 | FSI=0.31-0.06Z4-1.16Z82+1.47Z8+2.04Z93-2.98Z92+1.31Z9+0.19Z102+0.05Z10 |
2004 | FSI=0.28+0.03Z42-0.09Z4+0.25Z83-1.42Z82+1.47Z8+2.19Z93-3.32Z92+1.56Z9+0.11Z103+0.01Z102+0.12Z10 |
2005 | FSI=-0.06-0.04Z4-1.01Z82+1.26Z8+3.38Z93-5.54Z92+2.91Z9+0.20Z102+0.27Z10 |
2006 | FSI=0.34-0.13Z33+0.18Z32-0.05Z3-0.11Z4-1.18Z82+1.53Z8+2.65Z93-3.95Z92+1.7Z9+0.26Z103-0.17Z102+0.13Z10 |
2007 | FSI=0.39-0.09Z4-0.35Z63+0.49Z62-0.15Z6-0.96Z82+1.28Z8+2.40Z93-3.51Z92+1.50Z9+0.22Z102-0.01Z10 |
2008 | FSI=0.54-0.14Z4-1.22Z82+1.61Z8+0.30Z92-0.13Z9+0.21Z103-0.08Z102+0.11Z10 |
2009 | FSI=0.39+0.12Z33-0.17Z32+0.06Z3-0.11Z4-0.14Z63+0.20Z62-0.08Z6-1.14Z82+1.47Z8+2.13Z93-3.26Z92+1.46Z9 +0.26Z103-0.11Z102+0.10Z10 |
2010 | FSI=0.38+0.02Z42-0.12Z4+0.82Z63-1.07Z62+0.26Z6-1.18Z82+1.52Z8+2.47Z93-3.79Z92+1.69Z9+0.24Z103 -0.14Z102+0.10Z10 |
2011 | FSI=-0.02-0.05Z4-3.32Z63+3.91Z62-0.61Z6-1.13Z82+1.45Z8+3.10Z93-5.10Z92+2.62Z9+0.45Z103-0.56Z102+0.47Z10 |
2012 | FSI=0.35-0.06Z4-0.06Z63+0.07Z62-0.01Z6-1.03Z82+1.39Z8+1.96Z93-3.01Z92+1.34Z9+0.33Z103-0.27Z102+0.23Z10 |
2013 | FSI=0.46-0.04Z4+0.32Z83-0.77Z82+0.63Z8+1.69Z93-2.21Z92+0.68Z9+0.29Z103-0.30Z102+0.24Z10 |
2014 | FSI=0.49-0.04Z4+0.97Z83-1.90Z82+1.24Z8+1.33Z93-1.70Z92+0.49Z9+0.29Z102-0.09Z10 |
Table 8
Influence coefficient of each factor"
Year | Annual precipitation | Annual average temperature | Chemical fertilizer applied per unit land area | Per capita GDP | Proportion of the population with access to clean water | Political stability and non-violence level |
---|---|---|---|---|---|---|
2002 | - | -0.0216 | - | 0.3192 | 0.372 | 0.2262 |
2003 | - | -0.057 | - | 0.319 | 0.378 | 0.243 |
2004 | - | -0.0682 | - | 0.297 | 0.4366 | 0.2436 |
2005 | - | -0.0354 | - | 0.2538 | 0.7518 | 0.4713 |
2006 | -0.0104 | -0.1104 | - | 0.3472 | 0.3978 | 0.22 |
2007 | - | -0.0924 | -0.0087 | 0.318 | 0.3885 | 0.2128 |
2008 | - | -0.138 | - | 0.384 | 0.1689 | 0.2324 |
2009 | 0.0048 | -0.1056 | -0.0066 | 0.3245 | 0.3276 | 0.252 |
2010 | - | -0.1005 | 0.012 | 0.336 | 0.363 | 0.1955 |
2011 | - | -0.10 | -0.64 | 0.82 | 0.61 | 0.36 |
2012 | - | -0.056 | -0.00029 | 0.354 | 0.2886 | 0.2871 |
2013 | - | -0.038 | - | 0.185 | 0.161 | 0.2379 |
2014 | - | -0.0378 | - | 0.312 | 0.1224 | 0.2035 |
[1] | An Y M, Zhao W W , 2012. Global climate change and food security: Review of the 2012 planet under pressure international conference. Acta Ecologica Sinica, 32(15):4940-4942. (in Chinese) |
[2] | Andrea B, Mauro E D, Carlo C , 2017. National food security assessment through the analysis of food consump-tion data from household consumption and expenditure surveys: The case of Brazil’s Pesquisa de OrçamentoFamiliares 2008/09. Food Policy, 72:20-26. |
[3] | Bach H, Bird J, Clausen T J et al., 2012. Transboundary River Basin Management: Addressing Water, Energy and Food Security. Mekong River Commission MRC, 12-13. Available online: (accessed on 29 September 2014) |
[4] | Beghin J, Meade B, Rosen S , 2017. A food demand framework for international food security assessment. Journal of Policy Modeling, 39:827-842. |
[5] | Food and Agriculture Organization of the United Nations, 2002. The State of Food Insecurity in the World 2001. Rome, Italy: Food and Agriculture Organizationof the United Nations. |
[6] | Food and Agriculture Organization of the United Nations, United Nations Children’s Fund, World Food Programme et al., 2017. The State of Food Security and Nutrition in the World 2017: Enhancing Resilience and Promoting Peace and Food Security. Rome, Italy: Food and Agriculture Organization of the United Nations, 4. |
[7] | Future Earth Interim Secretariat (FEIS), 2013. Future Earth Initial Design. Paris: International Council for Science. |
[8] | Future Earth Interim Secretariat (FEIS), 2014. Future Earth 2025 Vision. Paris: International Council for Science. |
[9] | IPCC Working Group III, 2007. Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. Geneva, Switzerland: Intergovernmental Panel on Climate Change. |
[10] | Jiang L, Xu F B , 2011. Review on climate change and food security. International Information, 5:37-42. (in Chinese) |
[11] | Kang S Z , 2014. Towards water and food security in China. Chinese Journal of Eco-Agriculture, 22(8):880-885. (in Chinese) |
[12] | Karabulut A A, Crenna E, Sala S et al., 2018. A proposal for integration of the ecosystem-water-food-land-energy (EWFLE) nexus concept into life cycle assessment: A synthesis matrix system for food security. Journal of Cleaner Production, 172:3874-3889. |
[13] | Li Z P, Li D C, Zhang T L , 2001. Threat and strategies of soil degradation to food security. Bulletin of Soil and Water Conservation, 21(4):65-69. (in Chinese) |
[14] | Liu Y, Zhao W W, Zhang X , 2016. Promoting the delivering on the Environmental Dimension of the 2030 Agenda for Sustainable Development: Summary of the second session of the United Nations environment assembly. Acta Ecologica Sinica, 36(12):3843-3846. (in Chinese) |
[15] | Liu Y S, Li Y H , 2017. Revitalize the world’s countryside. Nature, 548(7667):275-277. |
[16] | Liu Y X, Zhao W W , 2013. Future Earth: Research programme on global sustainability. Acta Ecologica Sinica, 33(23):7610-7613. (in Chinese) |
[17] | Liu Y X, Zhao W W, Wang J , 2015. Coordinated response to global change for sustainable development: Future Earth 2025 Vision. Acta Ecologica Sinica, 35(7):2414-2417. (in Chinese) |
[18] | Luo X L, Zhang Y, Yang H D , 2006. Definition of food security in China and its evaluation. Journal of Shandong Agricultural University, 30(3):14-18. (in Chinese) |
[19] | Maxwell S, Smith M , 1992. Household food security: A conceptual review. In: Maxwell S et al.Household food security: Concepts, indicators, measurements: A technical review. UNICEF and IFAD, 1-6. |
[20] | Rahib H A, Kaan U, Umit I et al., 2016. Assessment of food security risk level using type 2 fuzzy system. Procedia Computer Science, 102:547-554. |
[21] | Shao L M , 2011. China grain safety early-warning system. Agricultural Economics and Management, 2:10-19. (in Chinese) |
[22] | Stephens E C, Jones A D, Parsons D , 2017. Agricultural systems research and global food security in the 21st century: An overview and roadmap for future opportunities. Agricultural Systems, 1:1-6. |
[23] | Thomas W H, Uris L C B , 2016. Attaining food and environmental security in an era of globalization. Global Environmental Change, 41:195-205. |
[24] | Wu W B, Tang H J, Yang P et al., 2010. Model-based assessment of food security at a global scale. Acta Geographica Sinica, 65(8):907-918. (in Chinese) |
[25] | Zhu X X, Fang X Q, Gao Y , 2012. Assessment of the food security in China based on system science. Chinese Journal of Agricultural Resources and Regional Planning, 33(6):11-17. (in Chinese) |
[1] | LI Wei, LI Xiaoyan, HUANG Yongmei, WANG Pei, ZHANG Cicheng. Spatial patch structure and adaptive strategy for desert shrub of Reaumuria soongorica in arid ecosystem of the Heihe River Basin [J]. Journal of Geographical Sciences, 2019, 29(9): 1507-1526. |
[2] | Huan WANG, Jiangbo GAO, Wenjuan HOU. Quantitative attribution analysis of soil erosion in different geomorphological types in karst areas: Based on the geodetector method [J]. Journal of Geographical Sciences, 2019, 29(2): 271-286. |
[3] | Chengjin WANG, Peiran CHEN, Yunhao CHEN. The identification of global strategic shipping pivots and their spatial patterns [J]. Journal of Geographical Sciences, 2018, 28(9): 1215-1232. |
[4] | Shaojian WANG, Jieyu WANG, Yang WANG. Effect of land prices on the spatial differentiation of housing prices: Evidence from cross-county analyses in China [J]. Journal of Geographical Sciences, 2018, 28(6): 725-740. |
[5] | Xueyan ZHAO, Weijun WANG, Wenyu WAN. Regional differences in the health status of Chinese residents: 2003-2013 [J]. Journal of Geographical Sciences, 2018, 28(6): 741-758. |
[6] | Jiawen FANG. An analysis of the differentiation rules and influencing factors of venture capital in Beijing-Tianjin-Hebei urban agglomeration [J]. Journal of Geographical Sciences, 2018, 28(4): 514-528. |
[7] | Ying WANG, Qigen LIN, Peijun SHI. Spatial pattern and influencing factors of landslide casualty events [J]. Journal of Geographical Sciences, 2018, 28(3): 259-374. |
[8] | Sijing LIU, Guoqi LI, Fengjun JIN. Quantitative measurement and development evaluation of logistics clusters in China [J]. Journal of Geographical Sciences, 2018, 28(12): 1825-1844. |
[9] | Jiaming LI, Yu YANG, Jie FAN, Fengjun JIN, Wenzhong ZHANG, Shenghe LIU, Bojie FU. Comparative research on regional differences in urbanization and spatial evolution of urban systems between China and India [J]. Journal of Geographical Sciences, 2018, 28(12): 1860-1876. |
[10] | Xiangli WU, Shan MAN. Air transportation in China: Temporal and spatial evolution and development forecasts [J]. Journal of Geographical Sciences, 2018, 28(10): 1485-1499. |
[11] | Zhongfa ZHOU, Shaoyun ZHANG, Kangning XIONG, Bo LI, Zhonghui TIAN, Quan CHEN, Lihui YAN, Shizhen XIAO. The spatial distribution and factors affecting karst cave development in Guizhou Province [J]. Journal of Geographical Sciences, 2017, 27(8): 1011-1024. |
[12] | Ziyan YAO, Lijuan ZHANG, Shihao TANG, Xiaxiang LI, Tiantian HAO. The basic characteristics and spatial patterns of global cultivated land change since the 1980s [J]. Journal of Geographical Sciences, 2017, 27(7): 771-785. |
[13] | Degen WANG, Yu NIU, Feng SUN, Kaiyong WANG, Jia QIAN, Feng LI. Evolution and spatial characteristics of tourism field strength of cities linked by high-speed rail (HSR) network in China [J]. Journal of Geographical Sciences, 2017, 27(7): 835-856. |
[14] | Huimin YAN, Fang LIU, Jiyuan LIU, Xiangming XIAO, Yuanwei QIN. Status of land use intensity in China and its impacts on land carrying capacity [J]. Journal of Geographical Sciences, 2017, 27(4): 387-402. |
[15] | Changjian WANG, Fei WANG, Xiaolei ZHANG, Hongou ZHANG. Influencing mechanism of energy-related carbon emissions in Xinjiang based on the input-output and structural decomposition analysis [J]. Journal of Geographical Sciences, 2017, 27(3): 365-384. |
|