The current study was carried out to test and identify the impact of climate change on food security in Elnuhood locality. 62 households’ participants were randomly selected through questionnaire field survey during 2012/2013-2013/2014 cropping seasons. The multi stage-stratified random sample technique was applied. Household economy approach (HEA), Linear programming (L.P), Partial crop budget, Dominance analysis, Marginal analysis, Sensitivity analysis, Time series analysis, Linear regression (L.R) and correlation coefficients as empirical approaches were applied. The households economy approach for the daily energy received per person per day in k. calories was calculated being 2242. According to WHO minimum rate of 2300 calories per person per day, this results implies that the households is marginally food insecure. Results of Linear programming (L.P) revealed a total of SDG 33706 as net income by producing and optimized only sorghum on the total of 9 hectares of land. The net crop income from this results was found lesser than the minimum livelihood requirement by 445%. Therefore, households are unable to mel the minimum livelihood requirement under the present climate conditions. Partial crop budget revealed that Higher net benefits in SDG were determined by cowpea (2999) followed by okra (2928) while a lower net return was obtained by watermelon (SDG 87). The dominance analysis results rendered 4 of nine treatment unacceptable for investment as five are other treatments with higher net returns of lower costs thereby leaving five treatments for the marginal rate of return (MRR) analysis. Analysis of marginal rate of returns revealed that T3 (Cowpea) was higher than minimum acceptable rate of return. Therefore treatment T2 and T3 (millet and cowpea) were emerged as the best among the alternatives and they had positive marginal rate of return of 150.9 and 378.3 %, respectively. Accordingly every SDG 1.00 invested in crop production, farmer can expect to recover the SDG 1.00 and earned additional SDG 1.509. Sensitivity analysis that assuming costs over run by 10% keeping the benefits same, and benefits reduction by 10% keeping costs same founded that T3 (cowpea) was the best and highly stable with MRR 352% while that of benefit short fall by 10% indicated also T3 was stable with MRR 348.6 %. Linear regression results shows that p-values of trend of the average maximum temperature was significant at five percent from zero level for groundnut and Roselle while total rainfall showed noticeable significance at five percent from zero level for cowpea and watermelon with Adjusted R2 of 79, 21, 31 and 20% respectively. This implies that the impact of climate change on food security variation can be explained by climatic factors. Results also revealed that climate has no impact on millet, sorghum, and sesame. This is highlights that variation in crop production as well as food security attributed to other non-climatic factors such as lack of extension and access to credit amongst households. In addition to examining descriptive statistics and analyzing linear trend between time and climatic variables and changes in trend (upward or downward) over the whole period (2000-2013). Results founded that there were significant and positive trend between total crop production and time at five percent from zero level on millet, cowpea and watermelon. This means that variation in production impacted by climate change during the long period. Correlation coefficient results showed that values of millet, sorghum, sesame, groundnut and Roselle production were weakly and negatively correlated with time and average maximum temperature. Cowpea and watermelon were significant at 0.5 percent from zero level with time and temperature. Groundnut production was significantly correlated (0.01) with total rainfall. However, Roselle has significant correlation at five percent from zero level with total rainfall. A weak and negative correlation relationship exists between millet, sorghum, groundnut, Roselle and watermelon and total rainfall. Moderately correlation showed by sesame and temperature. While smallest (0.195 and 0.182) positive correlation was given by sorghum and sesame against total rainfall. The above analysis implies that the effect of climate on grain production in the study area is not significant. However, the effect of average maximum temperature and rainfall on cowpea, time, groundnut and Roselle were respectively significant.