CHAPTER ONE
1.0  INTRODUCTION
1.1  Background of the Study                                                                                                    
Agriculture is one of the most important sector of the Nigerian economy which accounts for about 42% of Nigeria’s Gross Domestic Product (GDP) and over 70% of non-oil exports. It provides over 80% of the food needs of the country. About 70% of Nigerians live in rural area, and 90% of these are engaged in agriculture. This implies that agriculture is a key sector that stands to affect majority of Nigerians positively (Okolo, 2004). Despite its high contribution to the overall economy, this sector has been seriously facing challenges of many factors of which climate-related disasters such as drought and floods are the major ones (Deressa, 2008). According to Udofia (2001), the frightening effects of climate variability on the entire environment has reached a global dimension. Although its effects and the ecological and economic consequences are well perceived, they appear not to have been given the serious attention they deserve.
Climate is defined as the statistical description interms of mean and variability of relevant quantities over a period ranging from months to thousands or millions of years. The classical period is 30yrs as defined by the World Meteorological Organisation (WMO, 1992).These quantities are most often surface variables such as temperature, precipitation, and wind. The difference between climate and weather is that climate is what you expect while weather is what you get. The term climate variability denotes the natural characteristics of climate that manifests itself within the changes of climate with regards to time. Intergovernmental Panel on Climate Change (2001) defines climate variability as “variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all temporal and spatial scales beyond that ofindividual weather events”. This definition allows the consideration of climate change as a low frequency component of climate variability that can be managed using the same quantitative tools and research approaches (Mertz and Stone, 2003). Climate variation can enhance or diminish a local area’s comparative advantage in agriculture.
Changes in soil water availability, the increased occurrence of climate fluctuation, climate extremes and crop diseases, could lead to an overall reduction in crop yield and serious food shortage. Projections suggest that by the end of the 21stcentury, climate variability would have had substantial impact on crop production (Slater et al 2007). Humanity cannot accurately predict what the next season will bring but farmers, input suppliers, marketers and government would all like to know because it is critical to decision making. Climate variability has created uncertainties in temperature, rainfall and wind patterns. As a result, rural people in countries like Nigeria whose main occupation or economic activity is agriculture, are faced with so many challenges in decision making with respect to their agricultural activities ( Barnwal and Kotani, 2010).
 Climate variability adaptation methods according to Nyong, Adesina and Osman-Elasha (2007) are those strategies that enable the individual or the community to cope with or adjust to the impacts of the change in climate. Although, Zeirvogel et al (2008) had noted that the world has been undergoing series of adaptation in response to climate variability; the current climate change is expected to present heightened risk, new combinations of risks and potentially grave consequences. As such, adaptation has been identified as a policy option to mitigate the adverse effects of climate variability on farm productivity.  In agriculture, adaptation helps farmers achieve their food, income and livelihood security objectives in the face of changing climatic and socio-economic conditions including climatic variability, extreme weather conditions such as droughts, floods and volatile short term changes in local and large scale markets (Kandlinkar and Risbey, 2000).