India and China are two similar developing countries with huge populations, rapid economic growth and limited natural resources, therefore facing the massive pressure of ensuring food security. In this paper, we will discuss the food security situations in these two countries by studying the historical changes of food supply-demand balance with the concept of agricultural land requirements for food (LRF) from 1963-2009. LRF of a country is a function of population, per capita consumption/diet, cropping yield and cropping intensity. We have attempted to discuss and compare our results in a framework which links consumption of different groups of food items to diet patterns; then, to the total land requirement for food in a scenario when population is growing rapidly and diet diversification and urbanization due to economic reform impose excessive pressure on food security of both countries. We also elaborate on the role of technology dissemination and critically analyze the achievements and drawbacks of government policies to ensure food self-sufficiency and food security of OPEN ACCESSSustainability 2015, 7 5372 nations. Our results show that the total LRF increases approximately by 42% and 40%, whereas per capita LRF decreases significantly by about 48% and 30% from 1963-2009, for India and China, respectively. Furthermore, our studies reveal that population growth dominates most of the increase in total LRF for India; whereas diet pattern change induced by income growth drives the major increase in LRF for China. Therefore, sustainable management of agricultural land resource is an urgent need both for India and China as there will be demand for more food to meet the diet requirement for the entire population. We also demonstrate the role of India and China in future global food security programs and the challenges to implement the new land reform policies domestically.

Consumption of fossil fuel resources leads to global warming and climate change. Apart from the negative impact of greenhouse gases on the climate, the increasing emission of anthropogenic heat from energy consumption also brings significant impacts on urban ecosystems and the surface energy balance. The objective of this work is to develop a new method of estimating the global anthropogenic heat budget and validate it on the global scale with a high precision and resolution dataset. A statistical algorithm was applied to estimate the annual mean anthropogenic heat (AH-DMSP) from 1992 to 2010 at 1×1 km2 spatial resolution for the entire planet. AH-DMSP was validated for both provincial and city scales, and results indicate that our dataset performs well at both scales. Compared with other global anthropogenic heat datasets, the AH-DMSP has a higher precision and finer spatial distribution. Although there are some limitations, the AH-DMSP could provide reliable, multi-scale anthropogenic heat information, which could be used for further research on regional or global climate change and urban ecosystems.

Anthropogenic heat generated by human activity contributes to urban and regional climate warming. Due to the resolution and accuracy of existing anthropogenic heat data, it is difficult to analyze and simulate the corresponding effects. This study exploited a new method to estimate high spatial and temporal resolutions of anthropogenic heat based on long-term data of energy consumption and the US Air Force Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data from 1992 to 2010 across China. Our results showed that, throughout the entire study period, there are apparent increasing trends in anthropogenic heat in three major metropoli, i.e., the Beijing-Tianjin region, the Yangzi River delta and the Pearl River delta. The annual mean anthropogenic heat fluxes for Beijing, Shanghai and Guangzhou in 2010 were 17 Wm−2, 19 and 7.8 Wm−2, respectively. Comparisons with previous studies indicate that DMSP-OLS data could provide a better spatial proxy for estimating anthropogenic heat than population density and our analysis shows better performance at large scales for estimation of anthropogenic heat.

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