the most recent survey in 2013 and we found quite some differences that suggest that farmers are indeed climate-responsive although we cannot judge to what degree these changes fit the metaphor of “climate-smart”.
To answer the second question we used a Heckman model that allows joint esti- mation of a selection and an outcome equation, separately for the two countries.
Based on the results we can confirm that perceptions can be reasonably linked to farmers’ decision to undertake adaptation measures. In the model for Vietnam we can show that perceptions are shaped by the respondent’s characteristics, location variables and recent climate related shocks. Unfortunately, results for the Thailand model are less convincing. However, the climate-related shock variable is signifi- cant and consistent with the results in Vietnam. Similar results were found for the outcome equation where again the Vietnam model was more convincing. The dif- ference could be attributed to the lower awareness among the Thai farmers as shown in the lower number of cases in spite of largely equal initial sample size between the two countries. From an objective point of view, Vietnam is indeed more exposed to climate change due to its geographic location along the South China Sea costal line.
Finally, the answer to the third question is that the factors that drive specific cli- mate change related adaption measures differ among practices, provinces and coun- tries. They are to be found in the characteristics of the respondent and the household head whenever there is a difference between the two. Perhaps the most important factor in explaining specific adaptation measures are the three specific climate vari- ables namely rainfall, temperatures and wind, which are all significantly correlated with tree plantation. While for the other adaptation measures such as crop diversifi- cation, varietal change, etc. factors other than climate change may be more impor- tant, the clearest connection we find is with trees.
We believe our results can provide important information to policy makers and agricultural extension services who should improve their understanding of the farm- ers’ interpretation of climate change and the constraints that have so far prevented them from undertaking more and better adaption measures. Further studies should take a more in-depth look at those constraints and provide a detailed assessment of the costs and benefits of farmer-based adaption measures.
References
Asfaw A, Admassie A (2004) The role of education on the adoption of chemical fertilizer under different socioeconomic environments in Ethiopia. Agricultural Economics 30:215–228 Asian Development Bank (ADB) (2009) The Economics of Climate change in Southeast Asia: A
Regional Review. Asian Development Bank, Manila
Boonpragob K (2005) Crisis or Opportunity: Climate Change Impacts and Thailand. Greenpeace Southeast Asia, Thailand
Boonyawat J, Chiwanno S (2007) Origin and One Decade of Global Change Study in Thailand. In:
Boonyawat J (ed) Southeast Asia START Regional Center and a Decade of Global Change in Thailand. Southeast Asia Global Change System for Analysis, Research and Training.
Croppenstedt A, Demeke M, Meschi MM (2003) Technology adoption in the presence of constraints: The case of fertilizer demand of Ethiopia. Review of Development Economics 7:58–70
Cuong N (2008) Viet Nam Country Report—A Regional Review on the Economics of Climate Change in Southeast Asia. Report submitted for RETA 6427: A Regional Review of the Economics of Climate Change in Southeast Asia. Asian Development Bank, Manila
Dasgupta S, Laplante B, Meisner C et al (2007) The Impact of Sea Level Rise on Developing Countries: A Comparative Analysis. World Bank Policy Research Working Paper 4136, Deressa T, Hassan RM, Alemu T et al (2008) Analyzing the Determinants of Farmer's Choice
of Adaptation Methods and Perceptions of Climate Change in the Nile Basin of Ethiopia.
International Food Policy Research Institute Discussion Paper 00798
Dohmen T, Falk A, Huffman D et al (2011) Individual risk attitudes: Measurement, determinants, and behavioral consequences. Journal of the European Economic Associations 9(3):522–550 Feder G, Just RE, Zilberman D (1985) Adoption of Agricultural Innovations in Developing
Countries: A Survey. Economic Development and Cultural Change 33(2):255–298
Franzel S (1999) Socioeconomic factors affecting the adoption potential of improved tree fallows in Africa. Agroforestry Systems 47:305–321
Fuster J (2002) Physiology of executive functions: The perception-action cycle. In: Stuss DT, Knight R (eds) Principles of the frontal lobe. Oxford University Press, New York, p 96–108 Gbetibouo GA (2009) Understanding Farmers' Perceptions and Adaptations to Climate Change
and Variability: The case of the Limpopo Basin, South Africa. International Food Policy Research Institute Discussion Paper 00849.
Greene, W. H. (2003). Econometric Analysis. New Jersey: Pearson Education.
Hardeweg B, Menkhoff L, Waibel H (2013) Experimentally Validated Survey Evidence on Individual Risk Attitudes in Rural Thailand. Economic Development and Cultural Change 61:859–888
Heckman J J (1979) Sample Selection as a Specification Error. Econometrica 47:153–161 Hung PT, Trung LD, Cuong N (2010) Poverty of the Ethnic Minorities in Vietnam: Situation and
Chanlleges from the Poorest Communes. Munich Personal RePEc Archive.
Iglesias A, Quiroga S, Diz A (2011) Looking into the Future of Agriculture in a Changing Climate.
European Review of Agricultural Economics 38(3):427–447
IPCC (2014) Climate Change 2014: Impacts, Adaptation and Vulnerability. Part B: Regional Aspects. Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, New York
Jesdapipat S (2008) Thailand Country Report—A Regional Review on the Economics of Climate Change in Southeast Asia. Report submitted for RETA 6427: A Regional Review of the Economics of Climate Change in Southeast Asia. Asian Development Bank, Manila
Kahneman D, Knetsch JL, Thaler RH (1990) Experimental Tests of the Endowment Effect and the Coase Theorem. The Journal of Political Economy 98(6): 1325–1348
Long JS, Freese J (2006) Regression Models for Categorical Dependent Variables Using Stata (2nd ed). Stata Press, Texas
Maddison D (2007) The Perception of and Adaptation to Climate Change in Africa. Policy Reseach Working Paper WPS4308, The World Bank
Norris E, Batie S (1987) Virginia farmers' solid conservation decisions: An application of Tobit analysis. Southern Journal of Agricultural Economics 19:89–97
Puhani, PA (2000) The Heckman Correction for sample selection and its critique. Journal of Economic Surveys 14(1):53–68
Reilly JM, Schimmelpfennig D (1999) Agricultural impact assessment, vulnerability and the scope for adaptation. Climate Change 43:745–788
Tversky A, Kahneman D (1992) Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty 5(4):297–323
Waibel H, Tongruksawattana S, Voelker M (2013) Voices of the poor in climate change in Thailand and Vietnam. In: Ananta A, Bauer A, Thant M (eds) The Environments of the Poor in Southeast Asia, East Asia and the Pacific. Asian Development Bank, Singapore, p 170–186
Weber EU (2010) What shapes perceptions of climate change? Wires Climate Change 332–342 Wooldridge, JM (2009) Introductory Econometrics: A modern approach. South-Western Cengage
Learning, p 562
Open Access This chapter is distributed under the terms of the Creative Commons Attribution- NonCommercial- ShareAlike 3.0 IGO license (https://creativecommons.org/licenses/by-nc-sa/3.0/
igo/), which permits any noncommercial use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the Food and Agriculture Organization of the United Nations (FAO), provide a link to the Creative Commons license and indicate if changes were made. If you remix, transform, or build upon this book or a part thereof, you must distribute your contributions under the same license as the original. Any dispute related to the use of the works of the FAO that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the FAO’s name for any purpose other than for attribution, and the use of the FAO’s logo, shall be subject to a separate written license agreement between the FAO and the user and is not authorized as part of this CC-IGO license. Note that the link provided above includes additional terms and conditions of the license.
The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
161
© FAO 2018
L. Lipper et al. (eds.), Climate Smart Agriculture, Natural Resource Management and Policy 52, DOI 10.1007/978-3-319-61194-5_8