A multilevel analysis of drought risk in Indian agriculture: implications for managing risk at different geographical levels

  • Pratap S. BirthalEmail author
  • Jaweriah Hazrana
  • Digvijay S. Negi


Drought is an important downside risk in Indian agriculture; and the spatial differences in its intensity and probability of occurrence are considerable. To develop strategies to manage the risk of drought, and to coordinate and implement these strategies, it is essential to understand the variation in drought risk across geographical or administrative levels. This paper, using a multilevel modeling approach, decomposes the variation in drought risk across states, regions, districts, villages and households, and finds it disproportionately distributed. About half the variation is attributed to between-individual (i.e., household) differences and the rest to between-population differences, mainly to states and villages. These findings suggest the potential for a critical role of states (policies) and local institutions (communities) in enhancing resilience of agriculture to droughts through the correct targeting of policies and support for the most appropriate geographic level.


Drought risk Multiple geographical levels Agriculture Adaptations India 



  1. Aditya KS, Khan MT, Kishore A (2018) Adoption of crop insurance and impact: insights from India. Agric Econ Res Rev 31(2):163–174CrossRefGoogle Scholar
  2. Agrawal A, Perrin N, Chhatre A, Benson CS, Kononen M (2012) Climate policy process, local institutions, and adaptation actions: mechanisms of translation and influence. WIREs Clim Chang 3:565–579CrossRefGoogle Scholar
  3. Akerlof GA (1997) Social distance and social decisions. Econometrica 65(5):1005–1028CrossRefGoogle Scholar
  4. Amare M, Jensen ND, Shiferaw B, Cisse JD (2018) Rainfall shocks and agricultural productivity: implication for rural household consumption. Agric Syst 166:79–89CrossRefGoogle Scholar
  5. Anselin L (2002) Under the hood: issues in the specification and interpretation of spatial regression models. Agric Econ 27(3):247–267CrossRefGoogle Scholar
  6. Aryal JP, Sapkota TB, Stirling CM, Jat ML, Jat HS, Rai M, Mittal S, Sutaliya JM (2016) Conservation agriculture-based wheat production better copes with extreme climate events than conventional tillage-based systems: a case of untimely excess rainfall in Haryana, India. Agric Ecosyst Environ 233:325–335CrossRefGoogle Scholar
  7. Barbieri AF, Pan WK (2012) People, land, and context: multilevel determinants of off-farm employment in the Ecuadorian Amazon. Popul Space Place 19(5):558–579CrossRefGoogle Scholar
  8. Birthal PS, Hazrana J (2019) Crop diversification and resilience of agriculture to climatic shocks: evidence from India. Agric Syst 173:345–354CrossRefGoogle Scholar
  9. Birthal PS, Negi DS, Khan MT, Agarwal S (2015) Is Indian agriculture becoming resilient to droughts? Evidence from rice production systems. Food Policy 56:1–12CrossRefGoogle Scholar
  10. Birthal PS, Negi DS, Hazrana J (2019) Trade-off between risk and returns in farmers’ choice of crops: evidence from India. Agric Econ Res Rev 32(1):11–23CrossRefGoogle Scholar
  11. Britton M (1990) Geographical variation in mortality since 1920 for selected causes. In: Britton M (ed) Mortality and geography: a review in the mid-1980's for England and Wales. HMSO, LondonGoogle Scholar
  12. Campbell BM, Vermeulen SJ, Aggarwal PK, Corner-Dolloff C, Girvetz E, Loboguerrero AM, Ramirez-Villegas J, Rosenstock T, Sebastian L, Thornton P, Wollenberg E (2016) Reducing risks to food security from climate change. Glob Food Secur 11:34–43CrossRefGoogle Scholar
  13. Carey RK (2007) Modeling N2O emissions from agricultural soils using a multi-level linear regression. Duke University, DurhamGoogle Scholar
  14. Carter M, de Janvry A, Sadoulet E, Sarris A (2014). Index-based weather insurance for developing countries: a review of evidence and a set of propositions for up-scaling. Background document for the workshop “microfinance products for weather risk management in developing countries: state of the arts and perspectives”. Paris, June 25Google Scholar
  15. Curtis SL, Diamond I, McDonald JW (1993) Birth interval and family effects on post neonatal mortality in Brazil. Demography 30(1):33–43CrossRefGoogle Scholar
  16. Das P, Bhuyan-Aranyak H (2013) Policy and institutions in adaptation to climate change – case study on flood mitigation infrastructure in India and Nepal. ICIMOD working paper 2013/4, International Centre for Integrated Mountain Development, Kathmandu, NepalGoogle Scholar
  17. Dercon S (1996) Risk, crop choice, and savings: evidence from Tanzania. Econ Dev Cult Chang 44(3):485–513CrossRefGoogle Scholar
  18. Di Falco S, Chavas JP (2008) Rainfall shocks, resilience and the dynamic effects of crop biodiversity on the productivity of the agroecosystems. Land Econ 84(1):83–96CrossRefGoogle Scholar
  19. Durlauf SN (1996) Statistical mechanics approaches to socioeconomic behavior. NBER technical working paper 0203, National Bureau of Economic Research, IncGoogle Scholar
  20. Easterling W, Aggarwal P, Batima P et al (2007) Food, fibre and forest products. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Climate change 2007: impacts, adaptation and vulnerability. Cambridge University Press, Cambridge, pp 273–313Google Scholar
  21. Fan Y, Massey R, Park SC (2018) Multi-crop production decisions and economic irrigation water use efficiency: the effects of water costs, pressure irrigation adoption, and climatic determinants. Water 10(11):1–26Google Scholar
  22. GoI (2009) Manual for drought management. Ministry of Agriculture and Farmers’ Welfare, Government of India, New DelhiGoogle Scholar
  23. GoI (2015) Situation assessment survey of agricultural households, 70th round. National Sample Survey Office, Ministry of Statistics and Programme Implementation, Government of India, New DelhiGoogle Scholar
  24. Goldstein H (2003) Multilevel Statistical Models, 3rd edn. Edward Arnold, London and Wiley, New YorkGoogle Scholar
  25. Goldstein H, Spiegelhalter DJ (1996) League tables and their limitations: statistical issues in comparisons of institutional performance. J R Stat Soc A159:505–513CrossRefGoogle Scholar
  26. Gray J, Jesson D, Goldstein H, Hedger K, Rasbash J (1995) A multilevel analysis of school improvement: changes in school performance over time. Sch Eff Sch Improv 6(2):97–114CrossRefGoogle Scholar
  27. Guo G (1993) Use of sibling data to estimate family mortality effects in Guatemala. Demography 30(1):15–32CrossRefGoogle Scholar
  28. Khanal AR, Mishra AK (2017) Enhancing food security: food crop portfolio choice in response to climatic risk in India. Global. Food Secur 12:22–30CrossRefGoogle Scholar
  29. Kumar V, Ladha JK (2011) Direct-seeding of rice: recent developments and future research needs. Adv Agron 111:297–413CrossRefGoogle Scholar
  30. Kurukulasuriya P, Kala N, Mendelsohn R (2011) Adaptation and climate change impacts: a structural Ricardian model of irrigation and farm income in Africa. Clim Chang Econ 2:149–174CrossRefGoogle Scholar
  31. Lamb R (2002) Weather risk, crop mix, and wealth in the semi-arid tropics. Department of Agricultural and Resource Economics, North Carolina State University, RaleighGoogle Scholar
  32. Macintyre S (1986) The patterning of health by social position in contemporary Britain: directions for sociological research. Soc Sci Med 23:393–415CrossRefGoogle Scholar
  33. Michler JD, Shivley G (2016) Agricultural production, weather variability, and technical change: 40 years of evidence from India. Paper presented in the annual meeting of the Agricultural and Applied Economics and Association at Boston, Massachusetts, July 31-August 2Google Scholar
  34. Neumann K, Stehfest E, Verburg PH, Siebert S, Muller C (2011) Exploring global irrigation patters: a multilevel modeling approach. Agric Syst 104:703–713CrossRefGoogle Scholar
  35. Overmars KP, Verburg PH (2006) Multilevel modelling of land use from field to village level in the Philippines. Agric Syst 89:435–456CrossRefGoogle Scholar
  36. Palanisami K, Mohan K, Kakumanu KR, Raman S (2011) Spread and economics of micro-irrigation in India: evidence from nine states. Econ Polit Wkly 46(26/27):81–86Google Scholar
  37. Pray C, Nagarajan L, Li L, Huang J, Hu R, Selvaraj KN, Napasintuwong O, Babu RC (2011) Potential impact of biotechnology on adaptation of agriculture to climate change: the case of drought tolerant rice breeding in Asia. Sustainability 3(10):1723–1741CrossRefGoogle Scholar
  38. Qian SS, Cuffney TF, Alameddine I, McMahon G, Reckhow KH (2010) On the application of multilevel modeling in environmental and ecological studies. Ecology 91(2):355–361CrossRefGoogle Scholar
  39. Rathore BMS, Sud R, Saxena V, Rathore LS, Rathore TS, Subrahmanyam VG, Roy MM (2014) Drought conditions and management strategies in India- country report. Presented at the regional workshop for Asia-Pacific on capacity development to support National Drought Management Policies Hanoi, UN water initiative, May 6–9Google Scholar
  40. Raudenbush SW, Bryk AS (2002) Hierarchical linear models: applications and data analysis methods (2nd ed). Sage Publications, Thousand OaksGoogle Scholar
  41. Rogers DH, Lamm FR, (2012) Kansas irrigation trends. In: Proceedings of the 24th Annual Central Plains Irrigation Conference, Colby, Kansas, February 21–22, 2012 Available from CPIA, 760 N. Thompson, Colby, Kansas, USAGoogle Scholar
  42. Roscigno VJ (1998) Race and reproduction of educational disadvantages. Soc Forces 76:1033–1060CrossRefGoogle Scholar
  43. Rosenzweig CA, Binswanger HP (1993) Wealth, weather risk and the composition and profitability of agricultural investments. Econ J 103(416):56–78CrossRefGoogle Scholar
  44. Salazar C, Ayalew H, Fisker P (2018) Weather shocks and spatial market efficiency: evidence from Mozambique. J Dev Stud:1–16. CrossRefGoogle Scholar
  45. Seo SN (2010) A micro econometric analysis of adapting portfolios to climate change: adoption of agricultural systems in Latin America. Appl Econ Perspect Policy 32(3):489–514CrossRefGoogle Scholar
  46. Seo SN, Mendelsohn R (2008) An analysis of crop choice: adapting to climate change in Latin American farms. Ecol Econ 67:109–116CrossRefGoogle Scholar
  47. Sharma PK, Bhushan L, Ladha JK, Naresh RK, Gupta RK, Balasubramanian BV, Bouman BAM (2002) Crop water relations in rice–wheat cropping under different tillage systems and water management practices in a marginally sodic, medium-textured soil. In: Bouman BAM, Hengsdijk H, Hardy B, Toung TP, Ladha JK (eds) Water-wise rice production. International Rice Research Institute, ManilaGoogle Scholar
  48. Singh NP, Anand B, Singh S, Khan A (2019) Mainstreaming climate adaptation in Indian rural development agenda: a micro-macro convergence. Clim Risk Manag 24:30–41CrossRefGoogle Scholar
  49. Subramanian S, Jones K, Duncan C (2003) Multilevel methods for public health research. In: Kawachi I, Berkman L (eds) Neighborhoods and health. Oxford Press, New YorkGoogle Scholar
  50. Taraz V (2017) Adaptation to climate change: historical evidence from the Indian monsoon. Environ Dev Econ 22(5):517–545CrossRefGoogle Scholar
  51. Thompson A, Robbins P, Sohngen B, Arvai J, Koontz T (2006) Economy, politics, and institutions: from adaptation to adaptive management in climate change. Clim Chang 78(1):1–5CrossRefGoogle Scholar
  52. Vance C, Iovanna R (2006) Analyzing spatial hierarchies in remotely sensed data: insights from a multilevel model of tropical deforestation. Land Use Policy 23(3):226–236CrossRefGoogle Scholar
  53. Young KR, Lipton JK (2006) Adaptive governance and climate change in the tropical highlands of western South America. Clim Chang 78:63–102CrossRefGoogle Scholar
  54. Zampieri M, Ceglar A, Dentener F, Toreti A (2017) Wheat yield loss attributable to heat-waves, drought and water excess at the global, national and subnational scales. Environ Res Lett 12:064008CrossRefGoogle Scholar
  55. Zhang LE, Liao C, Zhang H, Hua X (2018) Multilevel modeling of rural livelihood strategies from peasant to village level in Henan Province, China. Sustainability 10(2967):1–13Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Pratap S. Birthal
    • 1
    Email author
  • Jaweriah Hazrana
    • 1
  • Digvijay S. Negi
    • 2
  1. 1.National Institute of Agricultural Economics and Policy ResearchNew DelhiIndia
  2. 2.Indira Gandhi Institute of Development ResearchMumbaiIndia

Personalised recommendations