This paper provides one of the first empirical studies that examine the impact of climate change adaptation practices on technical efficiency (TE) among smallholder farmers in Nepal. An adaptation index is used to explore the impact of farmers’ adaptation on TE using the stochastic frontier analysis framework. Data for six districts of Nepal representing all three agro-ecological regions (terai, hill, and mountain) were collected from a focus group discussion, a stakeholder workshop and a household survey. The survey shows that about 91% of the farming households have adopted at least one practice to minimize the adverse impacts of climate change. Empirical results reveal that adaptation is an important factor explaining efficiency differentials among farming households. Those adopting a greater number of adaptation practices on a larger scale are, on average, found to be 13% more technically efficient than those adopting fewer practices on smaller scale. The empirical results also show that average TE is only 0.72, indicating that there are opportunities for farming households in Nepal to further improve productive efficiency, on average by 28%. Other important factors that explain variations in the productive efficiency across farming households include farmer’s education level, irrigation facilities, market access, and social capital such as farmer’s participations in relevant agricultural organizations and clubs. This study provides empirical evidence to policy makers that small scale adjustments made by farmers in response to climate change impacts are effective in improving farmers’ efficiency in agriculture production. This indicates a need for farmers’ involvement in climate change adaptation planning.
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The map of Nepal showing the ecological regions and the study districts is presented in Supplementary Figure 1.
A VDC is an administrative unit in Nepal similar to municipality which is further divided into nine wards. Each ward constitutes one several villages.
Total value of agricultural production is measured as the sum of the value of all the crops produced by a household. This includes the value of both the sold quantity and that kept in the house for family consumption. We used the market prices of all the produces as provided by the farmers.
These questions were included both in the FGD and the household survey questionnaire.
One FGD was conducted with farmers in each district to understand their perception of climate change and to identify climate change adaptation practices adopted in the study area.
One stakeholder workshop was organized in each ecological region to assess the importance of different adaptation practices identified through the FGD.
The null hypothesis here is that Cobb-Douglas functional form better fits the data than the Translog form. The LR statistics, LR = − 2(log Likelihood (H0)–log Likelihood (H1)), LR = 74.64, reject the null hypothesis.
The principal crops dominating the agricultural sector are rice, maize, and wheat together accounting for more than 90% of the cultivated area and food grain production in Nepal.
Aigner D, Lovell CK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econ 6:21–37
Akram N (2012) Is climate change hindering economic growth of Asian economies? Asia Pac Dev J 19:1–18
Asadullah MN, Rahman S (2009) Farm productivity and efficiency in rural Bangladesh: the role of education revisited. Appl Econ 41:17–33
Bandara JS, Cai Y (2014) The impact of climate change on food crop productivity, food prices and food security in South Asia. J Econ Anal Policy 44:451–465
Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econ 20:325–332
Below TB, Mutabazi KD, Kirschke D, Franke C, Sieber S, Siebert R, Tscherning K (2012) Can farmers’ adaptation to climate change be explained by socio-economic household-level variables? Glob Environ Chang 22:223–235
Binam JN, Sylla K, Diarra I, Nyambi G (2003) Factors affecting technical efficiency among coffee farmers in Cote d’Ivoire: evidence from the centre west region. Afr Dev Rev 15:66–76
Binam JN, Tonye J, Nyambi G, Akoa M (2004) Factors affecting the technical efficiency among smallholder farmers in the slash and burn agriculture zone of Cameroon. Food Policy 29:531–545
Chen Z, Huffman WE, Rozelle S (2009) Farm technology and technical efficiency: evidence from four regions in China. China Econ Rev 20:153–161
Chhetri N, Chaudhary P, Tiwari PR, Yadaw RB (2012) Institutional and technological innovation: understanding agricultural adaptation to climate change in Nepal. Appl Geogr 33:142–150
Coelli T (1996) A guide to FRONTIER version 4.1: a computer program for stochastic frontier production and cost function estimation: CEPA Working papers, No. 7/96. Department of Econometrics, University of New England, Armidale
Coelli T, Fleming E (2004) Diversification economies and specialisation efficiencies in a mixed food and coffee smallholder farming system in Papua New Guinea. Agric Econ 31:229–239
Deressa TT, Hassan RM, Ringler C, Alemu T, Yesuf M (2009) Determinants of farmers’ choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Glob Environ Chang 19:248–255
Di Falco S, Verones M, Yesuf M (2011) Does adaptation to climate change provide food security? A micro-perspective from Ethiopia. Am J Agric Econ 93:829–846
Esham M, Garforth C (2013) Agricultural adaptation to climate change: insights from a farming community in Sri Lanka. Mitig Adapt Strateg Glob Chang 18:535–549
FAO (2014) The state of food insecurity in the world: strengthening the enabling environment for food security and nutrition. Food and Agriculture Organization of the United Nations, Rome
Finger R, Hediger W, Schmid S (2011) Irrigation as adaptation strategy to climate change—a biophysical and economic appraisal for Swiss maize production. Clim Chang 105:509–528
Hassan R, Nhemachena C (2008) Determinants of African farmers’ strategies for adapting to climate change: multinomial choice analysis. Afr J Agric Resour Econ 2:83–104
Huang J, Wang Y, Wang J (2015) Farmers’ adaptation to extreme weather events through farm management and its impacts on the mean and risk of rice yield in China. Am J Agric Econ 97(2):602–617
IPCC (2001) Climate change 2001: the scientific basis. Intergovernmental Panel on Climate Change, Cambridge University Press
IPCC (2007) Climate Change 2007: impacts, adaptation and vulnerability: contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change (Vol. 4): Cambridge University Press
Janvry AD, Sadoulet E (2006) Progress in the modeling of rural households' behavior under market failures. In: Janvry Ad, Kanbur R (eds) Poverty, inequality and development: essay in honor of Erik Thorbecks. Springer, New York
Khanal U, Wilson C, Hoang VN, Lee B (2018) Farmers’ adaptation to climate change, its determinants and impacts on rice yield in Nepal. Ecol Econ 144:139–147
Kompas T, Che TN (2006) Technology choice and efficiency on Australian dairy farms. Aust J Agric Resour Econ 50:65–83
Lobell DB (2014) Climate change adaptation in crop production: beware of illusions. Glob Food Sec 3:72–76
Meeusen W, Van den Broeck J (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. Int Econ Rev 18:435–444
Mendelsohn R (2000) Efficient adaptation to climate change. Clim Chang 45:583–600
MoAD (2012) Statistical Information on Nepalese Agriculture 2011/12. Government of Nepal, Ministry of Agricultural Development, Agri-Business Promotion and Statistics Division, Agri statistics Section, Singha Durbar, Kathmandu
MoF (2013) Economic survey: fiscal year 2012/13. Ministry of Finance, Kathmandu
MoF (2014) Economic survey: fiscal year 2013/14. Ministry of Finance, Kathmandu
Neumann K, Verburg PH, Stehfest E, Müller C (2010) The yield gap of global grain production: a spatial analysis. Agric Syst 103:316–326
Niles MT, Brown M, Dynes R (2016) Farmer’s intended and actual adoption of climate change mitigation and adaptation strategies. Clim Chang 135:277–295
O’Donnell CJ, Rao DSP, Battese GE (2008) Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empir Econ 34:231–255
Omonona BT, Egbetokun O, Akanbi A (2010) Farmers resource-use and technical efficiency in cowpea production in Nigeria. J Econ Anal Policy 40:87–95
Rahman S (2011) Resource use efficiency under self-selectivity: the case of Bangladeshi rice producers. Aust J Agric Resour Econ 55:273–290
Rahman S, Rahman M (2009) Impact of land fragmentation and resource ownership on productivity and efficiency: the case of rice producers in Bangladesh. Land Use Policy 26:95–103
Sarker AR, Alam K, Gow J (2014) Assessing the effects of climate change on rice yields: an econometric investigation using Bangladeshi panel data. J Econ Anal Policy 44(4):405–416
Solís D, Bravo-Ureta BE, Quiroga RE (2009) Technical efficiency among peasant farmers participating in natural resource management programmes in Central America. J Agric Econ 60:202–219
Thiam A, Bravo-Ureta BE, Rivas TE (2001) Technical efficiency in developing country agriculture: a meta-analysis. Agric Econ 25:235–243
Wheeler T, Von Braun J (2013) Climate change impacts on global food security. Science 341:508–513
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Khanal, U., Wilson, C., Lee, B. et al. Do climate change adaptation practices improve technical efficiency of smallholder farmers? Evidence from Nepal. Climatic Change 147, 507–521 (2018). https://doi.org/10.1007/s10584-018-2168-4