Farmer perception of their environment is a factor of climate change. Adaptation to climate change requires farmers to realize that the climate has changed and they must identify useful adaptations and implement them. This study analyzed the per-ception of climate change among rural farmers in central agri-cultural zone of Delta State, Nigeria. Climate change studies often assume certain adaptations and minimal examination of how, when, why, and conditions under which adaptations usually take place in any economic and social systems. The study was conducted by survey method on 131 respondents using struc-tured interview schedule and questionnaire. Data were analyzed with descriptive statistics and linear regression model to test that education, gender, and farming experience influenced farmers’ perception of climate change. The results showed that the farmers were aware of climate change. The identified causes of climate change were ranging from intensified agriculture, population explosion, increased use of fossil fuel, loss of in-digenous know practice to gas flaring. The effects of climate change on crops and livestocks were also identified by the rural farmers. Many of the farmers adapted to climate change by planting trees, carrying out soil conservation practice, changing planting dates, using different crop varieties, installing fans in livestock pens, and applying irrigation. Almost half of them did not adapt to climate change. The linear regression analysis revealed that education, gender, and farming experience influ-enced farmers’ perception of climate change. The major barriers to adaptation to climate change included lack of information, lack of money, and inadequate land.<br /><br /><br />

This study was conducted to isolate the determinants of improved fish production technologies in Delta State, Nigeria. Data were collected from a sample population of 250 fish farmers from ten randomly selected Local Government Areas of Delta State. The data were elicited from respondents with the use of structured interview schedule while descriptive statistics and multiple regression analysis were used to analyze the data. The recommended fish farming technologies at different stages of adoption process were p H testing and regulation, testing of dissolved oxygen level, feed formulation, polyculture, practices integrated fish/poultry or rice farming, re-circulation method, cage system, spawning and stocking density. The level of adoption was low. The grand mean adoption score and adoption index are 1.02 and 0.10, respectively. The low level of adoption was attributed to cost of the technologies, their complexities and lack of extension contact. The level of education, age of farmers, farm size, farm income and extension contact were the major determinants of fish production technologies adoption at 0.05 level of significance.

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