Determinants of Farm-Level Adaptation Diversity to Cyclone and Flood: Insights from a Farm Household-Level Survey in Eastern India Chandra Sekhar Bahinipati Gujarat Institute of Development Research (GIDR) Ahmedabad – , India The World Weather Open Science Conference – The Weather: What’s the Outlook? 17 th August, 2014

Structure of the Presentation Introduction: - Review of Literature - Objective of the Present Study Farm-level Adaptation Diversity Empirical Approach Study Area - Odisha, India - Selection of Study Villages and Sample Farm Households Empirical Specification of Model Variables Results and Discussion Concluding Observations

Introduction Climate Extremes: Cyclonic Storms and Floods Increasing frequency and intensity of climatic extremes (IPCC, 2012), in the developing nations (Mirza, 2003; Stern, 2007; Botzen and van den Bergh, 2009; Fankhauser and McDermott, 2014) For Example, - Developing nations: amount of loss is 1% of GDP during , while 0.3% for low income nations, and less than 0.1% for high income nations (IPCC, 2012). - Mirza (2003): direct losses were annually US$ 35 billion during 1990s, which was eight times higher in comparison to the 1960s. India: - According to EM-DAT: damage costs due to floods and cyclones were US$ billion and US$ billion between 1970 and 2012, respectively. - While US$ 2.83 billion lost during the 1970s, it is increased to US$ billion during the 2010s; at the same time, the number of these events increased from 48 to Frequency and intensity of cyclonic storms and extreme rainfall events will increase in the years to come (Unnikrishnan et al., 2011; Guhathakurta et al., 2012).

Impact of Natural Disasters (major storms, floods and earthquakes)

Relatively higher impact on agriculture in India (Rao, 2010) – a higher percentage of households depend on agriculture (54.6% as of 2011 Census). Adaptation measures to climate extremes - Farm-level adaptation reduce the impacts by 30 to 100%, depending on the spatial scale (see Bahinipati, 2011). In fact, Indian farmers have been adapting to past extreme events (Jodha, 1991; Roy et al., 2002; Mwinjaka et al., 2010; Jodha et al., 2012) - Ability to adapt differs from farmer to farmer. Knowledge of the present adaptation practices and factors affecting farmer’s choice will have policy suggestions in the context of successful implementation of adaptation options, mainly in the disaster prone regions of India. Introduction

 Farmers’ adaptive behaviour – Africa, Latin America, China, and South Asia. Adapt or not adapt – logit/ probit model (Bryan et al 2009; Deressa et al 2011; Di Falco et al 2011; Panda et al 2013; Wood et al 2014) Various adaptation options - either mutually exclusive or inclusive. Options are not mutually exclusive – multivariate probit model (Nhemachena and Hassan, 2007; Piya et al 2013; Bahinipati and Venkatachalam, 2014). Options are mutually exclusive – multinomial logit model -choice of different crops (Kurukulasuriya and Mendelsohn, 2007; Seo and Mendelsohn, 2008; Wang et al 2010) -farm management adaptation practices (Hassan and Nhemachena, 2008; Gbetibouo, 2009; Deressa et al 2009; Hisali et al 2011; Gebrehiwot and van der Veen, 2013; Sarker et al 2013). Adaptation Diversity: Number of adaptation measures (ex-ante and ex-post)  No studies have so far examined this in the context of India, particularly with reference to climate extremes. Review of Literature

Objective:  To identify determinants of farm-level adaptation diversity to climate extremes. This could help the policy makers to influence farmers to undertake farm-level adaptation mechanisms in the disaster prone regions of India. Previous studies have investigated factors related to climate variables, household/ household head characteristics, and plot-level information. Present study – formal and informal institutions For empirical assessment, the state of Odisha, India is taken as a case study – prone to both cyclone and flood (Bhatta, 1997; Chittibabu et al., 2004; GoO, 2004). Objective of the Present Study

Farm-level Adaptation Diversity Farm-level adaptation diversity undertaken by farm households

Ordered Data – Ordered probit model widely applied in the crop-diversity literature (e.g., see Ndjeunga and Nelson, 2005; Nagarajan et al., 2005) As described in Wooldridge (2002), and Cameron and Trivedi (2005), the ordered probit model is based on latent regression and denoted as, Where represents latent and continuous measure of adaptation strategy by a farm household ‘h’, x h is a vector of explanatory variables, is a vector of parameters to be estimated, and ‘e’ describes a random error term, which follows a normal distribution. Here, is unobservable but we do have an observed choice, and is determined from the model as follows: Empirical Approach

The parameter represents thresholds or cut off points, which can be estimated along with the parameter. Given the standard normal assumption for e, we can derive the conditional distribution of y given x: Where, is the cumulative standard normal distribution. The ordered probit model can be estimated using maximum likelihood (ML). The log likelihood function is numerically maximised subject to. Further, to interpret the effects of explanatory variables on the probabilities, the marginal effects are derived as (Wooldridge, 2002): Cross-section analysis - Multi-collinearity (VIF: 1.14 to 6.82) and Heteroskedasticity (Robust standard error) The information was gathered at the household-level and not at plot-level. Empirical Approach

Study Area: Odisha State of Odisha – geographically situated at the Bay of Bengal During , both cyclones and floods have occurred for 126 years in Odisha (Bhatta, 1997; Chittibabu et al., 2004; GoO, 2004 and 2011) – nine years during (GoO, 2011). Increasing frequency and intensity of cyclone and flood in the recent years (Mohanty et al., 2008; Pasupalak, 2010; Guhathakurta et al., 2012). Economic loss: around INR 1.05 billion during 1970s, increased to INR 8.51 billion, INR billion and INR billion during 1990s, 2000s and , respectively. As per BMTPC (Building Materials and Technology Promotion Council) vulnerability atlas, Odisha’s 35.8 percent, 2.4 percent and 61.7 percent of the total area are at risk under a wind velocity of 55 m/s (meter per second) and 50m/s, 47m/s and 44 m/s, and 39m/s respectively (BMTPC, 2006). Of the state’s total area, 21 percent (i.e thousand hectares) is considered as flood prone (World Bank, 2008) – 75 percent is spread across eight districts, including six coastal districts, namely, Balasore, Bhadrak, Kendrapada, Jagatsinghpur, Puri and Ganjam, and two non-coastal districts, such as Cuttack and Jajpur (World Bank, 2008). An average of 0.33 million ha agricultural land was damaged in the state due to flood during (GoO, 2013)

Map of the Study Region

Reported Population Affected (in millions) Reported Economic Damage (Rs. in millions)

Flood related economic loss of Odisha during (Rs. in millions) Source: GoO (2004 and 2011) Unseasonal cyclonic rainfall in major crop loss across 24 districts in Odisha (Rs 60,000 million; GoO, 2011). Flood in September 2011 caused damages around INR million in the state (Samal, 2011). Severe Cyclonic Storms in 2013 – crop loss across 18 districts, which is calculated as INR 23,000 million, and an estimates loss to house, crops and public properties as INR 1,43,734.7 million (GoO, 2013)

Study Area: Selection of Study Villages and Sample Farm Households Within the state, three cyclone and flood prone districts were purposively selected, namely as Balasore, Kendrapada and Jajpur, for conducting a farm-household level survey. - For instance, these three districts have come across at least 20 cyclones and floods during , and among them, the Balasore district has experienced a higher number of these events, i.e. 30 times (GoO, 2011). A stratified random sampling method was used to select farm households with an aim to cover households representing different categories of land ownership.  In total, 285 farm households were interviewed.

Empirical Specification of Model Variables Description of the Independent Variables

Results and Discussion Table 3, 4 & 5

Concluding Observations Higher likelihood of undertaking adaptation diversity during ex-post period – This could be lack of prior information about the occurrence of cyclones and floods. Government investment on scientific modeling for prediction of cyclones and floods, so that farmers’ could undertake better adaptation decision. Cyclone affected farmers are likely to adopt higher levels of adaptation diversity – this may be because of non-availability adaptation measures to cope with flood events. Promotion of adaptation measures related to flood Further, size of household, farming experience, per capita income, agriculture as major source of income and received formal crop loss compensation are some of the important determinants. Organise exposure meetings and shared-learning dialogues with the experienced farmers Formal and informal institutions: crop loss compensation is found as a strong determinant - suggests that existing institutions could not play an important role to enhance farm-level adaptation diversity. This emphasises modification in the existing institutions to enhance farm-level adaptations, so that farmers can prevent expected crop loss due to cyclones and floods.

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