| dc.description.abstract | Climate change is occurring in Central America, and one manifestation is changes in the frequency and intensity of extreme events. According to the IPCC (Intergovernmental panel of climate change) the effects of climate change will be more severe along the Pacific coast of Central America where El Salvador is located. In El Salvador, most of the country’s production activities are based on its extensive natural ecosystems. Major sectors in which the impacts of climate change could be important are natural ecosystems, water resources, coastal zones, agriculture, economy, and human health. Climate change will augment the challenges in many dimensions. However, agriculture is inarguably the sector most affected by climate change (IPCC, 2007). For that reason, this study is designed in order to find the production trends of corn, bean, coffee and sugar cane in El Salvador and to find their correlations with climatic factors. On the other hand, in El Salvador, the agricultural sector is especially important in the economic development of the country. In this decade its contribution to the total GDP has always been higher than 11%, reaching 12% in 2014 (Avelar, 2015). Therefore, this study also analyses the impacts of the climatic factors on some economic indicators.
First, by using simple moving average, weighted moving average, and cumulative moving average as a forecasting approach for climatic, agricultural and economic indicators is carried out. After that, we explored and compared the accuracy of each moving average method by finding the root mean square error for each indicator. The results indicate that for the given variables the most accurate theory among three approaches is the simple moving average because the lower root mean square error results are obtained with it.
Finally, this study explores the correlations among the different climatic factors on the economic indicators and agricultural production indexes by applying a statistical analysis using scatter diagrams. The correlations among indicators are explained in details to conclude the three interrelations among climatic, agricultural and economic indicators.
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