This dissertation addresses: 1) the reliability of El Nino Southern Oscillation (ENSO) forecasts generated by the International Research Institute for Climate and Society (IRI) of Columbia University; 2) estimation of parameters of Mexican crop demand; and 3) the potential impacts of climate change on Mexican agriculture. The IRI ENSO forecasts were evaluated using prequential analysis, with calibration and scoring rules. Calibration tests and the Yates' decomposition measures of the Brier score suggest that the IRI ENSO forecasts are improving in reliability and skill, showing a learning by doing behavior, i.e., these IRI ENSO forecasts show improved ability to predict the ENSO phases that really happen. In terms of estimation of the parameters of Mexican crop demand, an LA/AIDS model was used but the results were not very satisfactory with statistical tests rejecting homogeneity and symmetry. Furthermore, the estimated uncompensated price and income elasticities were found to be located in the tail regions of the Monte Carlo simulated density functions, showing poor validation of the initial estimates under similar economic (price and consumption) circumstances. Finally, in terms of the potential impacts that climate change has on Mexican agriculture, two 2050 climate change scenarios were examined. The central result indicates that Mexico benefits from climate change under the IPCC ensemble results for the B1 scenario and would experience welfare losses under the ensemble results for the A2 scenario. Moreover, dryland hectareage would decrease and would be replaced by irrigated areas. Finally, producer's net income was found to decrease at the national level under both climate change scenarios. The results were generated using a mathematical programming sector model that was updated for the study.
