Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Nishad, Shivnarayan
- Quantification of Regional and Global Sustainability Based on Combined Resource Criticality of Land and Water
Abstract Views :270 |
PDF Views:70
Authors
Affiliations
1 CSIR-National Institute for Science, Technology and Development Studies (NISTADS), Dr K.S. Krishnan Marg, New Delhi 110 012, IN
2 Department of Mathematics, M.S. Ramaiah University of Applied Sciences, Peenya Campus, Bengaluru 560 058, IN
1 CSIR-National Institute for Science, Technology and Development Studies (NISTADS), Dr K.S. Krishnan Marg, New Delhi 110 012, IN
2 Department of Mathematics, M.S. Ramaiah University of Applied Sciences, Peenya Campus, Bengaluru 560 058, IN
Source
Current Science, Vol 114, No 02 (2018), Pagination: 355-366Abstract
The overall global food sustainability is limited by the simultaneous availability of primary resources at regional scales, although the international trade network can redistribute available (surplus) food. Assessments based on isolated resource (like water) or demand (like food) cannot provide correct estimates of sustainability. We define a novel criticality index on the basis of simultaneous regional availability of arable land and water to quantify sustainability. Analyses at regional and global scale show that while a relatively small fraction of world population is subcritical in terms of food availability, much larger fractions are becoming subcritical in terms of food production. The combined resource criticality implies stronger constraints for sustainability.Keywords
Agricultural Sustainability, Carrying Capacity, Criticality Index, Food Sustainability, Water Sustainability.References
- Pretty, J., Agricultural sustainability: concepts, principles and evidence. Philos. Trans. R. Soc. B. Biol. Sci., 2008, 363, 447–465.
- Goswami, P. and Nishad, S., Virtual water trade and time scales for loss of water sustainability: a comparative regional analysis. Sci. Rep., 2015, 5(9306), 1–11.
- Goswami, P. and Nishad, S., Dynamical formalism for assessment and projection of carrying capacity in different socio-climatic scenarios. Curr. Sci., 2015, 109(2), 280–287.
- Goswami, P. and Nishad, S., Assessment of agricultural sustainability in changing scenarios: a case study for India. Curr. Sci., 2014, 106, 552–557.
- Allen, P., Food for the Future: Conditions and Contradictions of Sustainability, John Wiley, New York, 1993.
- Helms, M., Food sustainability, food security and the environment. Brit. Food J., 2004, 106, 380–387.
- Konar, M. et al., Water for food: the global virtual water trade network. Water Resour. Res., 2011, 47, W0552.
- Odegard, I. R. Y. and Voet, E. V., The future of food – scenarios and the effect on natural resource use in agriculture in 2050. Ecol. Econ., 2014, 97, 51–59.
- Hamdy, A. and Trisrio-Liuzzi, G., How to achieve the required food production to meet the growing demand? New Medit., 2009, 8, 4–12.
- Gilland, World population and food supply: can food production keep pace with population growth in the next half-century. Food Policy, 2002, 27, 27–63.
- Godfray, H. C. J. et al., Food security: the challenge of feeding 9 billion people. Science, 2010, 327, 812–818.
- Godfray, H. C. J. et al., The future of the global food system. Philos. Trans. R. Soc. London B Biol. Sci., 2010, 365, 2769–2777.
- Kearney, J., Food consumption trends and drivers. Philos. Trans. R. Soc. B. Biol. Sci., 2010, 365, 2793–2807.
- Wirsenius, S., Azar, C. and Berndes, G., How much land is needed for global food production under scenarios of dietary changes and livestock productivity increases in 2030? Agric. Syst., 2010, 103, 621–638.
- Speth, J., Global food needs and resource limits. In Sustainability of Rice in the Global Food System (eds Dowling, N. G., Greenfield, S. M. and Ficsher, K. S.), Pacific Basin Study Center, International Rice Research Institute, Manila, Philippines, 1988, pp. 11–15.
- Cohen, J., Population growth and earth’s human carrying capacity. Science, 1995, 269, 341–346.
- Kastner, T., Rivas, M. J. I., Koch, W. and Nonhebel, S., Global changes in diets and the consequences for land requirements for food. Proc. Natl. Acad. Sci. USA, 2012, 109, 6868–6872.
- Doos, B. R., Population growth and loss of arable land. Glob. Environ. Change, 2002, 12, 303–311.
- Rulli, M. C., Saviori, A. and D’Odorico, P., Global land and water grabbing. Proc. Natl. Acad. Sci. USA, 2013, 110, 892–897.
- Liu, Y. S., Wang, J. Y. and Long, H. L., Analysis of arable land loss and its impact on rural sustainability in Southern Jiangsu Province of China. J. Environ. Manage., 2010, 91, 646–643.
- Hanjra, M. A. and Qureshi, M. E., Global water crisis and future food security in an era of climate change. Food Policy, 2010, 35, 365–377.
- Falkenmark, M., Growing water scarcity in agriculture: future challenge to global water security. Philos. Trans. R. Soc. A, 2013, 371, 20120410(1–4).
- Yang, H., Reichert, P., Abbaspour, K. C. and Zehnder, A. J. B., A water resources threshold and its implications for food security. Environ. Sci. Technol., 2003, 37, 3048–3054.
- Forouzani, M. and Karami, E., Agricultural water poverty index and sustainability. Agron. Sustain. Dev., 2011, 31, 415–431.
- Fereres, E., Orgaz, F. and Gonzalez-Dugo, V., Reflections on food security under water scarcity. J. Exp. Bot., 2011, 62, 4079–4086.
- Postel, S. L., Entering an era of water scarcity: the challenges ahead. Ecol. Appl., 2000, 10, 941–948.
- Xiong, et al., Climate change, water availability and future cereal production in China. Agric. Ecosyst. Enviorn., 2010, 135, 58–69.
- Gregory, P. J., Ingram, J. and Brklacich, M., Climate change and food security. Philos. T.R. Soc. B, 2005, 360, 2139–2148.
- Arnell, M. W., Climate change and global water resources. Global. Environ. Change, 1999, 9, S31–S49.
- Brown, M. E. and Chunk, S., Food security under climate change. Science, 2008, 319, 580–581.
- Vorosmarty, C. J., Douglas, E. M, Green, P. A. and Revenga, C., Geospatial indicators of emerging water stress: an application to Africa. Ambio, 2005, 34, 230–236.
- Sullivan, C. A. and Meigh, J. R., Considering the water poverty index in the context of poverty alleviation. Water Policy, 2003, 5, 513–528.
- Sandoval-Solis, S., McKinney, D. and Loucks, D., Sustainability index for water resources planning and management. J. Water Resour. Plan. Manage, 2011, 137, 381–390.
- Brown, A. and Matlock, M. D., A Review of Water Scarcity Index and Methodologies, White paper 106, the Sustainability Consortium, University of Arkansas, USA, 2011.
- Lawrence, P., Meigh, J. and Sullivan, C., The water poverty index: international comparisons. Keele Economic Research Paper, University of Keele, Staffordshire, 2002.
- Asheesh, M., Allocating the gaps of shared water resources (the scarcity index) case study Palestine Israel. In Water Resource in the Middle East (eds Shuval, H. and Dweik, H.), Springer, Berlin, Heidelberg, New York, 2007, pp. 241–248.
- Chaves, H. M. L. and Alipaz, S., An integrated indicator based on basin hydrology, environment, life and policy: the watershed sustainability index. Water Resour. Manage, 2007, 21, 883–895.
- Gleick, P. H., Basic water requirements for human activities: meeting basic needs. Water Intern., 1996, 21, 83–92.
- Brown, L. R. and Kane, H., Full House: Reassessing the Earth’s Population Carrying Capacity. W.W. Norton and Company, New York, 1994.
- Leonard, T. M., Encyclopedia of the Developing World, Routledge, Taylor and Francis Group, New York, 2006.
- FAOSTAT, 2010; http://faostat.fao.org/
- FAO AQUASTAT: Water Use, 2010; http://www.fao.org/nr/water/aquastat/water_use/index6.stm
- Food and Agriculture Organization of the United Nations, Soil loss accelerating worldwide: hinders effort to feed earth’s growing population. FAO, Washington, DC, 1993, p. 9.
- Smil, V. C., Energy, Food, Environment: Realities, Myths, Options, Clarendon Press, Oxford, UK, 1987.
- Zhao, W., Arable land change dynamics and their driving forces for the major countries of the world. Shengtai Xuebao/Acta Ecol. Sin., 2012, 32, 6452–6462.
- Xie, Y. M., Guanjin, T. and Xeurong, X., Socio-economic driving forces of arable land conversion: a case study of Wuxian City, China. Glob. Environ. Change, 2005, 15, 238–252.
- Gustavsson, J., Cederberg, C., Sonesson, U., Otterdijk, R. V. and Meybeck, A., Global Food Losses and Global Food Waste, FAO, Rome, 2011.
- A Dynamical Model of Growth of Membership to an Opinion
Abstract Views :241 |
PDF Views:76
Authors
Affiliations
1 Institute of Frontier Science and Applications, Bengaluru 560 037, IN
2 CSIR National Institute of Science, Technology and Development Studies, Dr K.S. Krishnan Marg, New Delhi 110 012, IN
1 Institute of Frontier Science and Applications, Bengaluru 560 037, IN
2 CSIR National Institute of Science, Technology and Development Studies, Dr K.S. Krishnan Marg, New Delhi 110 012, IN
Source
Current Science, Vol 116, No 4 (2019), Pagination: 577-591Abstract
Many social processes, from elections to terrorism, depend on growth of memberships to opinions. In a generic sense, an opinion is a proposition that for an individual has financial, cultural and emotional impli-cations. The individual responses in turn create a ‘social response’ which influences the individual response resulting in a dynamical system with two-way feed-backs. We consider a set of deterministic dynamical equations that describe individual response to a class of prescribed opinions. The time-dependent opinion dynamics model exhibits nearly complete acceptance to nearly complete rejection with complex evolution, providing the framework for a mechanistic descrip-tion of opinion formation.Keywords
Dynamic Model, Growth of Membership, Opinion Dynamics, Social Engineering.References
- Jalili, M., Social power and opinion formation in complex networks. Phys. A, 2013, 392(4), 959–966.
- Hegselmann, R. and Flache, A., Understanding complex social dynamics – a plea for cellular automata based modelling. J. Art. Soci. Soc. Simul., 1998, 1.
- Li, P. P. and Hui, P. M., Dynamics of opinion formation in hierar-chical social networks: networks structure and initial bias. Eur. Phys. J.B, 2008, 61, 371–376.
- Toral R. and Tessone, C. J., Finite size effects in the dynamics of opinion formation. Commun. Comput. Phys., 2007, 2, 177–195.
- Wu, F. and Huberman, B., Social structure and opinion formation. Comput. Econ., 2004, 0407002.
- Moussaid, M., Kammer, J. E., Analytis, P. P. and Neth, H., Social influence and the collective dynamics of opinion formation. PLoS ONE, 2013, 8(11), e78433.
- Krause, U., A discrete nonlinear and non-autonomous model of consensus formation. In Communications in Difference Equations (eds Flaydi, S. et al.), Gordon and Breach Publication, Amster-dam, 2000, pp. 227–236.
- Shang, Y., Consensus formation of two-level opinion dynamics. Acta Math. Sci., 2014, 34(4), 1029–1040.
- Takao, F., A simple model of consensus formation. Okayama Econ. Rev., 1999, 31, 95–100.
- Galam, S., The dynamics of minority opinions in democratic debate. Phys. A, 2004, 336, 56–62.
- Laguna, M. F., Abramson, G. and Zanette, D., Minorities in a model for opinion formation. Complexity, 2004, 9(4), 31–36.
- Mogilner, A., Edelstein-Keshet, L., Bent, L. and Spiros, A., Mutual interactions, potentials, and individual distance in a social aggre-gation. J. Math. Biol., 2003, 47, 353–389.
- Mobilia, M. and Redner, S., Majority versus minority dynamics: phase transition in an interacting two-state spin system. Phys. Rev. E, 2003, 68(4), 046106.
- Boccara, N., Models of opinion formation: influence of opinion leaders. Int. J. Mod. Phys. C, 2008, 19, 93–109.
- Crespi, B. J., The evolution of social behaviour in micro-organisms. Trends Ecol. Evol., 2001, 16(4), 178–183.
- Wollman, N. P. et al., The dynamics of animal social networks: analytical, conceptual, and theoretical advances. Behav. Ecol., 2013, 25(2), 242–255.
- Ghosh, R. and Lerman, K., The impact of network flows on com-munity formation in models of opinion dynamics. J. Math. Soc., 2015, 39(2), 109–124.
- Hoylst, J. A., Kacperski, K. and Schweitzer, F., Social impact models of opinion dynamics. Ann. Rev. Comput. Phys., 2001, IX, 253–273.
- Antal, T., Krapivsky, P. L. and Redner, S., Dynamics of social balance on networks. Phys. Rev. E, 2005, 72, 036121.
- Altafini, C., Dynamics of opinion forming in structurally balanced social networks. PLoS ONE, 2012, 7(6), e38135.
- Gueron, S., Levin, S. A. and Rubenstein, D. I., The dynamics of herds: from individuals to aggregations. J. Theor. Biol., 1996, 182(1), 85–98.
- Fieldhouse, E., Shryane, N. and Pickles, A., Strategic voting and constituency context: modelling party preference and vote in multiparty elections. Polit. Geogr., 2007, 26(2), 159–178.
- Jiao, Y., Syau, Y. R. and Lee, E. S., Fuzzy adaptive network in presidential elections. Math. Comput. Model., 2006, 43, 244–253.
- Berger, R. L., A necessary and sufficient condition for reaching a consensus using DeGischolar_main’s method. J. Am. Stat. Assoc., 1981, 76(374), 415–418.
- Kosse, K., Group size and societal complexity: thresholds in the long-term memory. J. Anthropol. Archaeol., 1990, 9, 275–303.
- Farine, D. R. et al., The role of social and ecological processes in structuring animal populations: a case study from automated track-ing of wild birds. R. Soc. Open Sci., 2015, 2, 150057.
- Belenky, A. S. and King, D. C., A mathematical model for esti-mating the potential margin of state undecided voters for a candi-date in a US Federal election. Math. Comput. Model., 2007, 45, 585–593.
- Mishra, A. K., A simple mathematical model for the spread of two political parties. Nonlinear Anal. Model. Control, 2012, 17, 343–354.
- Farley, J. D., Evolutionary dynamics of the insurgency in Iraq: a mathematical model of the battle for hearts and minds. Stud. Confl. Terror., 2007, 30(11), 947–962.
- Anderton, C. H. and Carter, J. R., On rational choice theory and the study of terrorism. Def. Peace Econ., 2005, 16, 275–282.
- Facchetti, G., Iacono, G. and Altafini, C., Computing global struc-tural balance in large-scale signed social networks. Proc. Natl. Acad. Sci., 2011, 108(52), 20953–20958.
- Latane, B. and Nowak, A., Self-organizing social systems, neces-sary and sufficient conditions for the emergence of clustering, consolidation, and continuing diversity. In Progress in Communi-cation Science: Advances in Persuasion (eds Barnet, G. and Bostner, F.), Norwood, NJ, USA, Ablex Publishing Corporation, 1997, pp. 43–74.
- Iacono, G. and Altafini, C., Monotonicity, frustration, and ordered response: an analysis of the energy landscape of perturbed large-scale biological networks. BMC Syst. Biol., 2010, 4(1), 83.
- Weisbuch, G., Deffuant, G. and Amblard, F., Persuasion dynamics. Phys. A, 2005, 353, 555–575.
- Kawachi, K., Deterministic models for rumor transmission. Non-linear Anal. Real World Appl., 2008, 9, 1989–2028.
- Hegselmann, R. and Krause, U., Opinion dynamics and bounded confidence: models, analysis, and simulation. JASSS, 2002, 5(3), 1–33.
- Klir, G. J. and Yuan, B., Fuzzy Sets and Fuzzy Logic; Theory and Applications, Prentice Hall PTR, New Jersey, 1995.
- Cao, S., Dehmer, M. and Shi, Y., Extremality of degree-based graph entropies. Inf. Sci., 2014, 278, 22–33.
- Chen, Z., Dehmer, M., Emmert-Streib, F. and Shi, Y., Entropy of weighted graphs with Randi’c weights. Entropy, 2015, 17(6), 3710–3723.
- Chen, Z., Dehmer, M. and Shi, Y., A note on distance-based graph entropies. Entropy, 2014, 16(10), 5416–5427.
- Chen, Z., Dehmer, M. and Shi, Y., Bounds for degree-based network entropies. Appl. Math. Comput, 2015, 265, 983–993.
- Saganowski, S. et al., Predicting community evolution in social networks. Entropy, 2015, 17(5), 3053–3096.