Academic journal article Eastern Africa Social Science Research Review

Sustaining Smallholder Farmers' Livelihoods through Rainfall-Deficit-Index-Based Crop Insurance in Drought-Prone Areas: Lessons from Ethiopia

Academic journal article Eastern Africa Social Science Research Review

Sustaining Smallholder Farmers' Livelihoods through Rainfall-Deficit-Index-Based Crop Insurance in Drought-Prone Areas: Lessons from Ethiopia

Article excerpt

1. INTRODUCTION

Farming is a financially risky business. On a daily basis, farmers in general and smallholder farmers in particular are confronted with an ever-changing landscape of possible price, yield, and other outcomes that affect their financial returns and overall welfare. The consequences of decisions or events are often not known with certainty until long after those decisions or events occur; and so, outcomes may be better or worse than expected. When aggregate crop output or export demand changes sharply, for example, farm prices fluctuate substantially and farmers may realise returns that differ greatly from their expectations. Thus, understanding risk is a key issue in helping farmers to make better decisions in risky situations and in providing useful information to policymakers in assessing the effectiveness of different types of risk protection tools (Harwood et al. 1999).

Weather-related risks, when not managed, adversely affect the economy and perpetuate poverty throughout the developing countries (Wenner 2005) because they cannot be easily predicted and are harder to mitigate. On the other hand, microfinance institutions do not provide credit to those people who are vulnerable to weather risks due to the fear that the loan will not be paid back (Miranda and Vedenov 2001; Barnett, Barrett and Skees 2008). These remain to be some of the driving forces of livelihood challenges for poor smallholder farmers in developing countries.

Poor smallholder farmers often exercise risk coping/transferring strategies (ex post responses) which include crop sharing, informal risk pooling, seeking non-farm income, sale of productive assets, reallocation of labour, and public relief (Walker and Jodha 2006; World Bank 2005). These are, however, inadequate for recovery and building resilient livelihoods (Walker and Jodha 2006). Thus, publicly provided or market-based formal mechanisms which transfer risks to other individuals or institutions or crops or regions are used (Cummins and Mahul 2008; Hazell, Pomareda and Valde 1986; Sakurai and Reardon 1997).

One of the different types of insurance policies developed to help farmers confronted with the adverse effects of crop production risk is agricultural or crop insurance (Cl) which Wenner (2005, 16) explains as:

... a financial contingency contract that transfers production risk from a producer to another party via the payment of a premium that reflects the true long-tenn cost of the insurer who is assuming the risks. The insurer pools the risks faced by a large number of individuals and covers losses incurred by any one individual in the pool. It serves to essentially protect assets, stabilize income, and smooth consumption.

A typical means of transferring risk in farming is through agricultural insurance or Cl projects, which is 'a financial tool to transfer production risk associated with farming to a third party risk off taker via payment of a premium that reflects at least the true long-term cost of the insurer assuming those risks' (World Bank 2006, 13). This is found to be an effective tool to manage risks, alleviate poverty, and foster economic development (Skees et al. 2006; GlobalAgRisk 2009; Warner et al. 2009; Hess, Wiseman and Robertson 2006). Unlike the traditional insurance mechanism in which the indemnities are paid based on the actual individual yield losses, this method uses an index (rainfall, temperature, wind speed, humidity, etc.) that often strongly correlates with the loss to pay indemnities (Barnett and Mahul 2007). Given the availability of objectively measured and easily implemented data, weather-index-based insurance (WIBI) system related to rainfall is commonly used (GlobalAgRisk 2009; Hellmuth et al. 2009) across farm enterprises and countries.

In WIBI, the usual practices remain to be the construction of an index level that serves as a proxy for the threshold level of crop yield losses on the basis of a critical analysis of historical rainfall data, which help determine the range of indemnity and make payout when the realised value of the index exceeds the threshold. …

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