De-climatizing food security: Lessons from climate change micro-simulations in Peru
Autoři:
Gustavo Anríquez aff001; Gabriela Toledo aff001
Působiště autorů:
Pontificia Universidad Católica de Chile, Departamento de Economía Agraria, Facultad de Agronomía e Ingeniería Forestal, Santiago, Chile
aff001; Center for the Socioeconomic Impact of Environmental Policies, Santiago, Chile
aff002
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222483
Souhrn
This paper brings advances in weather data collection and modeling, and developments in socioeconomic climate microsimulations to bear on the analysis of the implications of climate change (CC) in the design of public policies to combat food insecurity. It uses new downscaled predictions of future climate in 2050, derived from three Earth System Models calibrated with a new historical weather station dataset for Peru. This climate data is used in a three-stage socioeconomic microsimulation model that includes climate risk, and deals with the endogeneity of incomes and simultaneity of expected food consumption and its variability. We estimate the impact of CC on agricultural yields, and find results consistent and fully bounded within what the global simulations literature has found, with yields falling up to 13% in some regions. However, we show that these drops (and increases) in yields translate to much smaller changes in food consumption, and also surprisingly, to very minor impacts on vulnerability to food insecurity. The document explores what explains this surprising result, showing that in addition to characteristics that are specific to Peru, there are household and market mediating mechanisms that are available in all countries, which explain how changes in yields, and corresponding farm incomes have a reduced impact in vulnerability to food insecurity. Finally, in light of these findings, we explore which policies might have greater impact in reducing food insecurity in contexts of hunger prevalence.
Klíčová slova:
Agricultural workers – Agriculture – Climate change – Crops – Food consumption – Peru – Simulation and modeling – Climate modeling
Zdroje
1. IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Ipcc. 2014.
2. De Pinto A, Ulimwengu JM, editors. A thriving agricultural sector in a changing climate: Meeting Malabo Declaration goals through climate-smart agriculture. Washington, DC: International Food Policy Research Institute (IFPRI); 2017. http://dx.doi.org/10.2499/9780896292949
3. FAO. The State of Food and Agriculture: Climate chnage, agriculture and food security. FAO. 2016. ISBN: 978-92-5-107671-2 I
4. Biewald A, Lotze-Campen H, Otto I, Brinckmann N, Bodirsky B, Weindl I, et al. The Impact of Climate Change on Costs of Food and People Exposed to Hunger at Subnational Scale. Postdam; 2015.
5. Fischer G, Shah M, Van Velthuizen H. Climate Change and Agricultural Vulnerability. World. 2002; 152.
6. Nelson GC, Rosegrant MW, Palazzo A, Gray I, Ingersoll C, Robertson R, et al. Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options. IFPRI. 2010.
7. Parry M, Rosenzweig C, Livermore M. Climate change, global food supply and risk of hunger. Philos Trans R Soc B Biol Sci. 2005;360: 2125–2138. doi: 10.1098/rstb.2005.1751 16433098
8. Nelson GC, Valin H, Sands RD, Havlík P, Ahammad H, Deryng D, et al. Climate change effects on agriculture: Economic responses to biophysical shocks. Proc Natl Acad Sci. 2014;111: 3274–3279. doi: 10.1073/pnas.1222465110 24344285
9. Porter JR, Xie L, Challinor AJ, Cochrane K, Howden SM, Iqbal MM, et al. Food security and food production systems. Climate Change 2014: Impacts, Adaptation, and Vulnerability PartA: Global and Sectoral Aspects Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2014.
10. Nelson GC, van der Mensbrugghe D, Ahammad H, Blanc E, Calvin K, Hasegawa T, et al. Agriculture and climate change in global scenarios: why don’t the models agree. Agric Econ. 2014;45: 85–101. doi: 10.1111/agec.12091
11. Wiebe K, Lotze-Campen H, Sands R, Tabeau A, van der Mensbrugghe D, Biewald A, et al. Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios. Environ Res Lett. 2015;10: 85010.
12. Devereux S, Edwards J. Climate Change and Food Security. IDS Bull. 2004;35: 22–30. doi: 10.1111/j.1759-5436.2004.tb00130.x
13. Seo SN. Micro-Behavioral Economics of Global Warming. Modeling Adaptation Strategies in Agricultural and Natural Resource Enterprises. Dordrecht, The Netherlands: Springer; 2015.
14. Mendelsohn R, Nordhaus WD, Shaw D. The Impact of Global Warming on Agriculture: A Ricardian Analysis. Am Econ Rev. 1994;84: 753–771.
15. van Wart J, Grassini P, Cassman KG. Impact of derived global weather data on simulated crop yields. Glob Chang Biol. 2013;19: 3822–34. doi: 10.1111/gcb.12302 23801639
16. Karfakis P, Knowles M, Smulders M, Capaldo J. Effects of global warming on vulnerability to food insecurity in rural Nicaragua. Rome; 2011. Report No.: ESA Working Paper No. 11–18.
17. Skoufias E, Vinha K. The impacts of climate variability on household welfare in rural Mexico. Popul Environ. 2013;34: 370–399. doi: 10.1007/s11111-012-0167-3
18. Asfaw S, Mortari AP, Arslan A, Karfakis P, Lipper L. Welfare Impacts of Climate Shocks: Evidence from Uganda. 2015.
19. Arslan A, McCarthy N, Lipper L, Asfaw S, Cattaneo A, Kokwe M. Climate Smart Agriculture? Assessing the Adaptation Implications in Zambia. J Agric Econ. 2015;66: 753–780. doi: 10.1111/1477-9552.12107
20. Di Falco S, Veronesi M, Yesuf M. Does Adaptation to Climate Change Provide Food Security? A Micro-Perspective from Ethiopia. Am J Agric Econ. 2011;93: 829–846. doi: 10.1093/ajae/aar006
21. Christiaensen LJ, Boisvert RN. On Measuring Household Food Vulnerability: Case Evidence from Northern Mali. Working Paper New York. 2000.
22. Morton JF. The impact of climate change on smallholder and subsistence agriculture. Proc Natl Acad Sci U S A. 2007;104: 19680–19685. doi: 10.1073/pnas.0701855104 18077400
23. Naiken L. Methodological Issues in the estimation of the prevalence of Undernourishment Based on Dietary Energy Consumption Data: A Review and Clarification. Rome; 2014. Report No.: 14–03.
24. Anríquez G, Daidone S, Mane E. Rising food prices and undernourishment: A cross-country inquiry. Food Policy. 2013;38: 190–202.
25. Saha A, Havenner A, Talpaz H. Stochastic production function estimation: small sample properties of ML versus FGLS. Appl Econ. 1997;29: 459–469. doi: 10.1080/000368497326958
26. Reyes García M, Gómez-Sánchez Prieto I, Espinoza Barrientos C, Bravo Rebatta F, Ganoza Morón L, Nutrición CN de A y. Tablas Peruanas de Composición de Alimentos. Lima: Ministerio de Salud; 2009.
27. Anriquez G, Daidone S, Karfakis P. Estimating the Nutritional Impacts of Soaring Food Prices at the Household Level. Rome; 2008.
28. WHO / FAO. Human energy requirements. In: FAO [Internet]. 2004 p. 107. http://www.fao.org/3/a-y5686e.pdf
29. Flato G, Marotzke J, Abiodun B, Braconnot P, Chou SC, Collins W, et al. Evaluation of Climate Models. Clim Chang 2013 Phys Sci Basis Contrib Work Gr I to Fifth Assess Rep Intergov Panel Clim Chang. 2013; 741–866.
30. SENAMHI, FAO. Statistical Downscaling of Climate Scenarios over Peru. Lima, Peru; 2014.
31. Diewert WE. Exact and superlative index numbers. Journal of Econometrics. 1976. pp. 115–145.
32. Sen AK. An Aspect of Indian Agriculture. Econ Wkly. 1962;14: 243–246.
33. Walsh RPD, Lawler DM. Rainfall Seasonality: Description, Spatial Patterns and Change through Time. Weather. 1981;36: 201–208. doi: 10.1002/j.1477-8696.1981.tb05400.x
34. Grömping U. Estimators of Relative Importance in Linear Regression Based on Variance Decomposition. Am Stat. 2007;61: 139–147. doi: 10.1198/000313007X188252
35. Just RE, Pope RD. Production Function Estimation and Related Risk Considerations. Am J Agric Econ. 1979;61: 276–284. doi: 10.2307/1239732
36. Davis B, Winters P, Carletto G, Covarrubias K, Quiñones EJ, Zezza A, et al. A Cross-Country Comparison of Rural Income Generating Activities. World Dev. 2010;38: 48–63. doi: 10.1016/j.worlddev.2009.01.003
37. Conforti P. Looking ahead in worLd food and agricuLture: Perspectives to 2050. FAO Rome. 2011.
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- Jak a kdy u celiakie začíná reakce na lepek? Možnou odpověď poodkryla čerstvá kanadská studie
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- Spermie, vajíčka a mozky – „jednohubky“ z výzkumu 2024/38
- Infekce se v Americe po příjezdu Kolumba šířily nesrovnatelně déle, než se traduje
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
Nejčtenější v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy